Intelligent agentic driver monitoring system for generating and executing a context-aware intervention

[0001] The present methods and systems are directed to urban intelligence solutions for intervening with a disengaged or distracted operator of a vehicle.

[0002] There is a need for an advanced system capable of generating and executing an intervention based on analyzing, using at least one intelligence model, sensor data associated with a vehicle.

[0003] The disclosed systems and methods may leverage urban intelligence technologies configured to utilize sensor data, including multi-sensor and real-time data, from environments like urban areas and roadways.

[0004] A disclosed method for utilizing an intelligent driver monitoring system to generate and execute a context-aware intervention comprises: receiving, using one or more computing device processors, from one or more multimodal sensors, at a first time, first sensor data, wherein the one or more multimodal sensors are associated with or comprised in at least one of: a vehicle or an operator of the vehicle; identifying, using the one or more computing device processors, the operator of the vehicle; accessing, using the one or more computing device processors, a first database comprising historical data associated with at least one of: the vehicle or the operator of the vehicle; initiating first analyzing, using the one or more computing device processors, based on first accessing at least one intelligence model, the first sensor data; determining, using the one or more computing device processors, based on the first analyzing, based on the first accessing the at least one intelligence model, the first sensor data, a first context associated with the operator of the vehicle; determining, using the one or more computing device processors, based on the first analyzing, based on the first accessing the at least one intelligence model, the first sensor data, a second context associated with the vehicle; determining, using the one or more computing device processors, based on the first context associated with the operator of the vehicle and the second context associated with the vehicle, first severity data; initiating generating, using the one or more computing device processors, based on second accessing the at least one intelligence model, a first intervention, wherein the first intervention is based on: the first context associated with the operator of the vehicle, the second context associated with the vehicle, the first severity data, and the historical data associated with at least one of: the vehicle or the operator of the vehicle; initiating execution of, using the one or more computing device processors, at a second time after the first time, the first intervention; receiving, using the one or more computing device processors, from the one or more multimodal sensors, at a third time after the first time and the second time, second sensor data; initiating second analyzing, using the one or more computing device processors, based on third accessing the at least one intelligence model, the second sensor data; determining, using the one or more computing device processors, based on the second analyzing, based on the third accessing the at least one intelligence model, the second sensor data, a third context associated with the operator of the vehicle; determining, using the one or more computing device processors, based on the second analyzing, based on the third accessing the at least one intelligence model, the second sensor data, a fourth context associated with the vehicle; determining, using the one or more computing device processors, based on the third context associated with the operator of the vehicle and the fourth context associated with the vehicle, the first intervention was successful; and determining, using the one or more computing device processors, based on the third context associated with the operator of the vehicle and the fourth context associated with the vehicle, feedback associated with the first intervention for fine-tuning the at least one intelligence model.

[0005] In some cases, the method further comprises: receiving, using the one or more computing device processors, from the operator of the vehicle or a supervisor of the operator of the vehicle, a first authorization or a first restriction associated with generating interventions; and initiating storage of, using the one or more computing device processors, in the first database, the first authorization or the first restriction associated with generating interventions received from the operator of the vehicle or the supervisor of the operator of the vehicle.

[0006] In other cases, the first context associated with the operator of the vehicle comprises or is associated with at least one of: an inattentiveness of the operator of the vehicle, a distraction affecting the operator of the vehicle, a medical ailment affecting the operator of the vehicle, a first activity associated with the operator of the vehicle, a physical state associated with the operator of the vehicle, or a cognitive state associated with the operator of the vehicle.

[0007] In yet other cases, the second context associated with the vehicle comprises or is associated with at least one of: a first passenger in the vehicle, a first behavior associated with the first passenger in the vehicle, a speed associated with the vehicle, lighting within the vehicle, noise levels within the vehicle, environmental conditions, external conditions, road conditions, weather conditions, a time of day, or at least one additional vehicle in a vicinity of the vehicle.

[0008] According to some embodiments, the first intervention is personalized based on the historical data associated with at least one of: the vehicle or the operator of the vehicle.

[0009] In other embodiments, the method further comprises initiating storage of, using the one or more computing device processors, in the first database comprising the historical data associated with at least one of: the vehicle or the operator of the vehicle, data associated with at least one of: the first intervention, the operator of the vehicle, or the feedback associated with the first intervention.

[0010] According to yet other embodiments, the one or more computing device processors are comprised in one or more computing systems, wherein the one or more computing systems are located in one or more locations.

[0011] One or more systems are disclosed for utilizing an intelligent driver monitoring system to execute a context-aware intervention, the one or more systems comprising: one or more computing databases; and one or more computing servers comprising one or more computing device processors and a memory storing instructions, the instructions being executable by the one or more computing device processors to: receive, from one or more multimodal sensors, at a first time, first sensor data, wherein the one or more multimodal sensors are associated with or comprised in at least one of: a vehicle or an operator of the vehicle; identify the operator of the vehicle; access a first database comprising historical data associated with at least one of: the vehicle or the operator of the vehicle; initiate first analyzing, using the one or more computing device processors, based on first accessing at least one intelligence model, the first sensor data; determine, based on the first analyzing, based on the first accessing the at least one intelligence model, the first sensor data, a first context associated with the operator of the vehicle; determine, based on the first analyzing, based on the first accessing the at least one intelligence model, the first sensor data, a second context associated with the vehicle; determine, based on the first context associated with the operator of the vehicle and the second context associated with the vehicle, first severity data; initiate generating, based on second accessing the at least one intelligence model, a first intervention, wherein the first intervention is based on: the first context associated with the operator of the vehicle, the second context associated with the vehicle, the first severity data associated with at least one of: the first context associated with the operator of the vehicle or the second context associated with the vehicle, and the historical data associated with at least one of: the vehicle or the operator of the vehicle; initiate execution of, using the one or more computing device processors, at a second time after the first time, the first intervention; receive, from the one or more multimodal sensors, at a third time after the first time and the second time, second sensor data; initiate second analyzing, using the one or more computing device processors, based on third accessing the at least one intelligence model, the second sensor data; determine, based on the second analyzing, based on the third accessing the at least one intelligence model, the second sensor data, a third context associated with the operator of the vehicle; determine, based on the second analyzing, based on the third accessing the at least one intelligence model, the second sensor data, a fourth context associated with the vehicle; determine, based on the third context associated with the operator of the vehicle and the fourth context associated with the vehicle, the first intervention was successful; and determine, based on the third context associated with the operator of the vehicle and the fourth context associated with the vehicle, feedback associated with the first intervention for fine-tuning the at least one intelligence model.

[0012] In one embodiment, the first sensor data comprises at least one of: visual data, auditory data, physiological data, contextual data, activity data, health data, or environmental data.

[0013] According to another embodiment, the second sensor data comprises at least one of: visual data, auditory data, physiological data, contextual data, activity data, health data, or environmental data.

[0014] In yet another embodiment, the first severity data is associated with at least one of: a state associated with the operator of the vehicle or an indicator based on the first context associated with the operator of the vehicle and the second context associated with the vehicle.

[0015] According to still another embodiment, the historical data comprises at least one of: at least one preference associated with the operator of the vehicle, feedback previously received from the operator of the vehicle, at least one response previously received from the operator of the vehicle, at least one setting associated with the operator of the vehicle, at least one condition associated with the vehicle, or personal data associated with the operator of the vehicle.

[0016] In another embodiment, the one or more systems are located in one or more locations.

[0017] Another disclosed method for utilizing an intelligent driver monitoring system to execute a context-aware intervention comprises: receiving, using one or more computing device processors, from one or more multimodal sensors, at a first time, first sensor data, wherein the one or more multimodal sensors are associated with or comprised in at least one of: a vehicle or an operator of the vehicle; identifying, using the one or more computing device processors, the operator of the vehicle; accessing, using the one or more computing device processors, a first database comprising historical data associated with at least one of: the vehicle or the operator of the vehicle; initiating first analyzing, using the one or more computing device processors, based on first accessing at least one intelligence model, the first sensor data; determining, using the one or more computing device processors, based on the first analyzing, based on the first accessing the at least one intelligence model, the first sensor data, a first context associated with the operator of the vehicle; determining, using the one or more computing device processors, based on the first analyzing, based on the first accessing the at least one intelligence model, the first sensor data, a second context associated with the vehicle; determining, using the one or more computing device processors, based on the first context associated with the operator of the vehicle and the second context associated with the vehicle, first severity data; initiating generating, using the one or more computing device processors, based on second accessing the at least one intelligence model, a first intervention, wherein the first intervention is based on: the first context associated with the operator of the vehicle, the second context associated with the vehicle, the first severity data associated with at least one of: the first context associated with the operator of the vehicle or the second context associated with the vehicle, and the historical data associated with at least one of: the vehicle or the operator of the vehicle; initiating execution of, using the one or more computing device processors, at a second time after the first time, the first intervention; receiving, using the one or more computing device processors, from the one or more multimodal sensors, at a third time after the first time and the second time, second sensor data; initiating second analyzing, using the one or more computing device processors, based on third accessing the at least one intelligence model, the second sensor data; determining, using the one or more computing device processors, based on the second analyzing, based on the third accessing the at least one intelligence model, the second sensor data, a third context associated with the operator of the vehicle; determining, using the one or more computing device processors, based on the second analyzing, based on the third accessing the at least one intelligence model, the second sensor data, a fourth context associated with the vehicle; determining, using the one or more computing device processors, based on the third context associated with the operator of the vehicle and the fourth context associated with the vehicle, second severity data; determining, using the one or more computing device processors, based on the third context associated with the operator of the vehicle, the fourth context associated with the vehicle, and the second severity data, the first intervention was unsuccessful; determining, using the one or more computing device processors, based on the third context associated with the operator of the vehicle and the fourth context associated with the vehicle, feedback associated with the first intervention for fine-tuning the at least one intelligence model; initiating generating, using the one or more computing device processors, based on fourth accessing the at least one intelligence model, a second intervention, wherein the second intervention is based on: the third context associated with the operator of the vehicle, the fourth context associated with the vehicle, the second severity data, the historical data associated with at least one of: the vehicle or the operator of the vehicle, and the feedback associated with the first intervention; and initiating execution of, using the one or more computing device processors, at a fourth time after the first time, the second time, and the third time, the second intervention.

[0018] According to some embodiments, at least one of: the first accessing the at least one intelligence model comprises transmitting the first sensor data to a first computing system associated with the at least one intelligence model, or the third accessing the at least one intelligence model comprises transmitting the second sensor data to the first computing system associated with the at least one intelligence model.

[0019] In other embodiments, the initiating execution of, using the one or more computing device processors, at the second time after the first time, the first intervention comprises transmitting the first intervention to a first computing system associated with the vehicle.

[0020] According to yet other embodiments, the second intervention comprises an escalation or a repetition of the first intervention.

[0021] In still other embodiments, the first intervention comprises at least one of: a lighting adjustment, a sound adjustment, an environment adjustment, a visual message, an audio message, a vibration, a haptic alert, an emergency call, a communication with a second vehicle, or seizing control of the vehicle.

[0022] According to other embodiments, the first intervention comprises at least one of: initiating a conversation with the operator of the vehicle, adjusting a volume level in the vehicle, adjusting a temperature setting in the vehicle, utilizing an audio assistant associated with the vehicle, triggering a vibration on a steering wheel or a seat comprised in the vehicle, initiating an emergency call, connecting with a second vehicle or a second operator associated with the second vehicle for assistance, executing a safe-stop protocol, engaging a driver assistance system, a vehicle to vehicle (V2V) intervention, or a vehicle to everything intervention (V2X).

[0023] In yet other embodiments, the one or more computing device processors are comprised in one or more computing systems, wherein the one or more computing systems are located in one or more locations.

[0029] Reference will now be made in detail to embodiments, examples of which are illustrated in the accompanying drawings and figures. In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the disclosed subject-matter. However, it will be apparent to one of ordinary skill in the art that the methods and systems disclosed may be practiced without these specific details.

[0030] As used herein, the term “exemplary” or “illustrative” means “serving as an example, an instance, or an illustration.” Any implementation described herein as exemplary or illustrative is not necessarily to be construed as advantageous and/or preferred over other embodiments. Unless the context requires otherwise, throughout the description and the claims, the word “comprise” and variations thereof, such as “comprises” and “comprising” are to be construed in an open, inclusive sense, i.e., as “including, but not limited to.”

[0031] Furthermore, the terms substantially real-time and near real-time, as used herein, refer to a processing and response timeframe that is close to, but not necessarily equal to, instantaneous or true real-time. While true real-time implies a delay of zero, any practical digital system will have some inherent latency due to data processing, computation, and network transmission. Therefore, these terms describe a timeframe where this inherent latency is sufficiently short as to be negligible for the effective operation of a given process or for the perception of a human user. The specific duration considered to be substantially real-time or near real-time is application-dependent and is defined by the requirements of the system to function as intended without being materially compromised by the delay.

[0032] For example, in different embodiments of this disclosure, the acceptable delay can vary significantly. In the context of streaming interactive video or augmented reality, near real-time may correspond to a total latency of less than 200 milliseconds, as longer delays can become perceptible and disruptive to a user. Thus, these terms encompass any timeframe in which the system's response is delivered quickly enough to be relevant and actionable for its specific purpose.

Network Environment

[0033] Shown in FIG. 1 is an exemplary network system 100 for executing the principles disclosed. Specifically, the exemplary network system 100 may be used to implement dynamic tracking of at least motive properties of a vehicle on a roadway. In the illustrated implementation, the network system 100 includes an analytics server system 105, one or more sensors 110, one or more computing databases 111a and 111b, and one or more output system 120, all of which are communicatively coupled to a network 115. Also connected to the network 115 are one or more endpoint devices 125a . . . 125n and an intelligence server 138.

[0034] It is appreciated that the implementation of the analytics server system 105 and/or the intelligence server 138 may be physically structured in various configurations. In one embodiment, the one or more computing device processors that execute the functions of the analytics server system 105 and/or the intelligence server 138 may be co-located at a single geographical location or housed within a single data center. Alternatively, the one or more computing device processors that execute the functions of the analytics server system 105 and/or the intelligence server 138 may be realized in a distributed computing environment, where multiple computing device processors are spread across disparate physical locations and are communicatively coupled over a network to operate in a coordinated or parallel fashion.

[0035] According to one embodiment, the analytics server system 105 includes a computing device such as a content server, a communication server, a laptop computer, a desktop computer, a handheld computing device, a tablet computing device, a virtual machine, a cloud-based computing system and/or a cloud-based service, and/or the like. The analytics server system 105 may include a plurality of computing devices configured to communicate with one another and/or communicate with other systems comprised in the network system 100 to implement the techniques described herein.

[0036] According to one embodiment, the analytics server system 105 comprises a data unit 136 (also referred to as one or more data units elsewhere herein) and at least one local computing database 111a. For example, results from implementing the disclosed methods may be stored in the local computing database 111a or in a remote computing database 111b that is physically distal (e.g., implemented in a first location that is different from a second location where the local computing database 111a is implemented). According to one embodiment, the local computing database 111a and the remote computing database 111b may include a non-volatile memory or similar permanent storage device and media. For example, the local computing database 111a and the remote computing database 111b can be a hard disk drive, a CD-ROM device, a DVD-ROM device, a DVD-RAM device, a DVD-RW device, a flash memory device, solid state media, or some other mass storage device known in the art for storing information on a more permanent basis.

[0037] The data unit 136 referenced above may include one or more instructions or computer logic that are executed by the one or more processors such as the processors discussed in association with FIGS. 2 and 3. The data unit 136 referenced herein comprises an executable software module containing one or more instructions or computer-executable logic. These instructions are specifically configured to be executed by one or more processors, such as the processors discussed in association with FIGS. 2 and 3, to perform the disclosed method. In particular, upon execution by said processors, the instructions within the data unit 136 causes a computing system to carry out the specific processing procedures, methods, techniques, and workflows provided in this disclosure. Thus, the data unit 136 represents a tangible software implementation that directs a computing device to function as a specialized machine for executing the disclosed method.

[0038] The analytics server system 105 may be configured to communicate with the one or more systems coupled to the network 115. This can include communications between the analytics server system 105 and the sensors 110 to transmit captured substantially real-time data (e.g., static images or dynamic images including or excluding a video stream, audio data, touch data, light detection and ranging (LiDAR) data, etc.) to the intelligence server 138 to resolve, classify, and identify features of objects (e.g., vehicles) captured by the sensors 110. In addition, this communication can involve the transmission of one or more alerts from the analytics server system 105 to the one or more endpoint devices 125a . . . 125n.

[0039] According to one embodiment, the analytics server system 105 can be coupled, via the network 115, to an intelligence server 138 configured to control or regulate, in conjunction with, or independent of the data unit 136, training of one or more computing models configured for implementing data classification and/or object identification computing operations. In some cases, the intelligence server 138 can comprise one or more intelligence units 140 that can implement computing operations such as: zero-shot learning computing operations, few-shot learning computing operations, and result/model fine-tuning computing operations. Additionally, or alternatively, one or more intelligent artificial intelligence (AI) models and/or machine learning (ML) models may comprise, or be based on at least one of: GPT-4, LLAMA-3, BLOOM, PaLM, GPT-3.5, BERT, Gemini, LaMDA, Perplexity, or Falcon. Additionally, or alternatively, one or more of the AI or ML models may also include multiple intelligence models and therefore may be configured to perform and/or execute multiple processes in parallel. In addition, the AI or ML models disclosed may include various artificial intelligence systems or structures, including but not limited to large language models (LLMs), deep learning models, machine learning models, neural networks (e.g., convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers), expert systems, decision trees, and reinforcement learning models.

[0040] Additionally, or alternatively, one or more of the AI/ML models may also include multiple intelligence models (e.g., separately trained intelligence models) and therefore may be configured to perform and/or execute multiple processes in parallel. In some embodiments, the intelligence server 138 may include a special chipset for processing large amounts of data and/or complex computing operations in a reduced amount of time. These chipsets may include, but are not limited to, Graphics Processing Units (GPUs), Tensor Processing Units (TPUs), Field-Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs) specifically designed for artificial intelligence (AI) workloads, or neuromorphic chips. Such chipsets can be configured to have parallel computing architectures, enabling efficient execution of matrix multiplications and convolutions, which comprise computing operations in a given intelligence model, particularly deep learning models. This parallel processing capability can allow for rapid ingestion, analysis, and processing of vast datasets, thereby accelerating model training, inference, and overall performance of the intelligence server 138 and/or analytics server system 105. The chipsets referenced herein may further incorporate dedicated memory architectures (e.g., High Bandwidth Memory (HBM)) optimized for the data throughput requirements of large intelligence models.

[0041] In some embodiments, the intelligence unit 140, or components thereof, may be implemented and/or deployed on dedicated hardware accelerators embedded within a system-on-chip (SoC) or as discrete integrated circuits. These hardware implementations can facilitate high-speed data processing and low-latency inference, needed for real-time applications such analyzing captured substantially real-time data. Furthermore, the intelligence server 138, or components thereof, including specialized chipsets and intelligence models, may be provided by a third-party vendor or service provider (e.g., via cloud-based AI/ML platforms) or may be developed and maintained in-house.

[0042] Once captured by the one or more sensors, the substantially real-time data may be transmitted to the analytics server system 105 and/or to the intelligence server 138 for processing. The analytics server system 105, acting as a central hub, can orchestrate the analysis of the data, while the intelligence server, powered by advanced AI and ML computing processes, performs the computationally intensive tasks. For example, the disclosed network system 100 might use a deep learning model of the intelligence server 138 to perform object detection on image data, identifying and classifying various objects in real-time. It could then employ a separate computing process to analyze the movement and behavior objects, recognizing complex interactions or anomalies. In some cases, the intelligence server 138 may combine data from multiple sensors 110 and can fuse a thermal image with a visual one to more accurately identify an object or use LiDAR data to provide precise spatial context to a 2D image. This integrated approach allows the network system 100 to extract a more comprehensive and nuanced understanding of the scene than would be possible with a single processing method, enabling a wide range of applications from security and surveillance to autonomous navigation and environmental monitoring.

[0043] It is appreciated that the one or more sensors 110 can comprise one or more visual sensors, audio sensors, touch sensors, radar sensors, LiDAR sensors, GPS sensors, vehicle telemetry sensors, specialized weather sensors, heat or thermal imaging sensors, 3D imaging sensors, AR sensors, VR sensors, and/or chemical sensors.

[0044] In some embodiments, the one or more sensors 110 can comprise visual sensors. These sensors may include any device or system capable of capturing, detecting, or measuring visual information from a monitored field. These devices or systems may include cameras, Closed-Circuit Television (CCTV) systems, imaging systems associated with or coupled to an unmanned aerial vehicle (UAV), emotion-optimized cameras, photodetectors, image sensors, satellite imaging sensors, and/or other optical devices. For example, the visual sensors may be configured to acquire visual data, which enables the identification, analysis, and monitoring of objects and environments in real time.

[0045] Furthermore, the one or more visual sensors may work with, or have integrated within their hardware setup, various illumination systems to optimize data capture, extending the one or more visual sensors capabilities far beyond standard daylight conditions. These illumination systems, which can be positioned proximally (e.g., a built-in flash on a camera) or distal (e.g., a high-powered floodlight or infrared emitter), work in tandem with the one or more visual sensors to improve the quality and utility of the acquired visual data.

[0046] The illumination systems referenced herein can encompass a wide range of technologies, each serving a specific purpose. For visible light imaging, active illumination like high-intensity LEDs or floodlights can be used to brighten a scene, ensuring clear and well-exposed images even in low-light conditions. These systems can be static or dynamic, adjusting their intensity based on the ambient light or focusing on a specific area of interest. For example, a streetlamp-style distal illuminator can provide broad coverage for a public space, while a proximal illuminator on a camera can provide a burst of light for a quick snapshot.

[0047] Expanding beyond the visible spectrum, the one or more visual sensors can have attendant infrared (IR) illumination systems, which are crucial for night vision and covert surveillance. These systems can emit IR light, which is invisible to the human eye but detectable by IR-sensitive cameras. This allows the one or more visual sensors to capture detailed images in complete darkness without alerting an imaged object (e.g., a vehicle) in the visual data to its presence. For example, these sensors can be configured for security applications or wildlife monitoring.

[0048] Furthermore, the one or more sensors 110 can incorporate other advanced illumination and sensing techniques. Thermal imaging sensors can be used in conjunction with the one or more visual sensors that highlight heat signatures, allowing for the detection of an object (e.g., a vehicle) even through smoke, fog, or camouflage. In some cases, thermal imaging sensors can beneficially enable search and rescue operations or identifying objects (e.g., a vehicle) in a chaotic environment.

[0049] In some instances, the one or more sensors 110 can comprise audio sensors. These sensors may include any device or system capable of detecting, capturing, or measuring sound waves or acoustic signals within a monitored environment. These devices or systems may include one or more of microphones, acoustic transducers, acoustic sensors, and/or ultrasonic detectors. For example, the sensors may be configured to identify, classify, and/or analyze sounds associated with objects, vehicles, and/or environmental conditions, and may be deployed in static or dynamic configurations to support a wide range of applications, including monitoring, diagnostics, and situational awareness.

[0050] In some embodiments, the one or more sensors 110 may comprise touch sensors. These sensors may include any device or system capable of measuring physical contact, pressure, or tactile input within a monitored environment. These devices or systems may incorporate one or more of capacitive, resistive, piezoelectric, and/or mechanical sensing elements. For example, touch sensors may be configured to detect and analyze physical interactions (e.g., a driver's hands on a steering wheel), which enhances monitoring, control, and operational diagnostics. It is appreciated that a touch sensor may comprise a privacy preserving floor sensor.

[0051] In some cases, the one or more sensors 110 may comprise radar sensors. These sensors may include any device capable of emitting electromagnetic waves and detecting their reflections to determine the presence, distance, speed, or movement of objects within a monitored area. These sensors may operate across various frequency bands and may be used for object detection, collision avoidance, or environmental mapping in both static and dynamic scenarios. Like the thermal imaging sensors above, radar sensors are especially useful in identifying an object in a chaotic environment.

[0052] In some embodiments, the one or more sensors 110 may comprise light detection and ranging, or LiDAR, sensors. These sensors may include any device configured to emit laser pulses and measure the reflected signals to generate spatial data, such as distance, shape, or surface characteristics of objects. LiDAR sensors may be utilized for three-dimensional mapping, object recognition, or environmental analysis, and may be deployed in fixed or mobile configurations. In some cases, LiDAR sensors beneficially provide precise spatial measurements and three-dimensional mapping (e.g., the curvature of a road), which are essential for object recognition and environmental analysis.

[0053] In some instances, the one or more sensors 110 may comprise global positioning system, or GPS, sensors. These sensors may include any device capable of receiving signals from global positioning satellites to determine geographic location, velocity, or timing information. Such devices may be integrated within vehicles or other platforms to provide real-time positioning data for navigation, tracking, or operational analysis. For example, the GPS sensors may be configured to determine an object's location (e.g., the location of a vehicle on a road).

[0054] In some cases, the one or more sensors 110 may comprise vehicle telemetry sensors. These sensors may include any device or system configured to monitor, record, or transmit data related to vehicle operation, performance, or status. This may include sensors for speed, acceleration, or braking (e.g., accelerometer), engine parameters, fuel consumption, or other operational metrics, and may support remote or local data acquisition. In some cases, the vehicle telemetry sensors may beneficially enable monitoring and transmitting data about a vehicle's proximity and relationship to other objects (e.g., another vehicle).

[0055] In some embodiments, the one or more sensors 110 may comprise specialized weather sensors. These sensors may include any device designed to detect, measure, or monitor environmental conditions such as temperature, humidity, precipitation, wind speed, atmospheric pressure, or other weather-related parameters. For example, specialized weather sensors may be configured to enhance situational awareness or inform operational decisions within the network system 100.

[0056] In some instances, the one or more sensors 110 may comprise heat or thermal imaging sensors. These sensors may include any device capable of detecting or measuring thermal energy or temperature variations within a monitored area. These sensors may utilize infrared, thermocouple, or other technologies to identify heat signatures, support object detection, or enable other types of environmental monitoring under various conditions.

[0057] In some cases, the one or more sensors 110 may comprise 3D imaging sensors. These sensors may include any device or system capable of capturing three-dimensional spatial data, including but not limited to stereoscopic cameras, structured light sensors, or time-of-flight sensors. In some cases, the 3D imaging sensors beneficially enable mapping of three-dimensional spaces (e.g., the curvature of a road), enabling advanced recognition, reconstruction, and analysis of objects and scenes.

[0058] In some embodiments, the one or more sensors 110 may comprise augmented reality (AR) sensors. These sensors may include any device or system capable of capturing, detecting, or measuring data that enables the overlay of digital information onto a physical environment. These devices or systems may comprise cameras, depth sensors, inertial measurement units (IMUs), and environmental sensors.

[0059] In some instances, the one or more sensors 110 may comprise virtual reality (VR) sensors. These sensors may include any device or system capable of capturing, detecting, or measuring data that enables the creation or manipulation of immersive digital environments. These devices or systems may comprise motion tracking sensors, gyroscopes, accelerometers, haptic feedback devices, and spatial mapping sensors. For example, the virtual reality sensors may be configured for monitoring user movements, gestures, and interactions.

[0060] In some cases, the one or more sensors 110 may comprise chemical sensors. These sensors may include any device or system capable of detecting, capturing, or measuring the presence, concentration, or composition of chemical substances within a monitored environment. These devices or systems may comprise smoke, electrochemical, optical, semiconductor, gas, and biological sensors. For example, the chemical sensors may be configured for detecting and analyzing chemical substances in an event venue or on a public road.

[0061] The different embodiments of the one or more sensors 110 disclosed herein are exemplary embodiments only.

[0062] The raw data from the one or more sensors 110 can comprise a substantially real-time data and may be subjected to a series of data conditioning including sophisticated signal processing and image optimization operations within the analytics server system 105 and/or the intelligence server 138. This step beneficially refines the raw data before it is analyzed.

[0063] A relevant process comprised in the data conditioning is noise reduction, which filters out random fluctuations in the raw data caused by, for example, low light or high sensor gain. Computing processes such as spatial and temporal filtering can be applied to create a cleaner image with less grain, improving the accuracy of subsequent AI and ML operations. For visual data captured with IR illumination, specialized algorithms can enhance the contrast and detail of heat signatures, making it easier to classify and identify objects.

[0064] In addition, the data conditioning phase may employ advanced computational photography techniques. This can include High Dynamic Range (HDR) imaging, where multiple images of a given scene (e.g., a captured view by the one or more sensors 110) are captured at different exposures and then combined to create a single image with detail in both the brightest and darkest areas. Additionally, image stitching can be used to combine multiple images from different sensors comprised in the one or more sensors into a single, comprehensive view of the scene, providing a wider field of view. The intelligence server 138 can also apply real-time image enhancement and sharpening filters to make the raw data more suitable for specific analytical tasks, such as facial recognition or object tracking, ensuring that every piece of data is optimized for its intended purpose.

[0065] The operational nature of the sensors 110 may vary, encompassing static and dynamic implementations. In some embodiments, the sensors 110 may be static, fitted to a fixed structure and secured to prevent any movement, such as a sensor permanently bolted to an elongated frame to monitor a given area on a roadway. Other static sensors may be fitted to a fixed structure but secured in a way that allows for movement. One such sensor may be a pan-tilt-zoom (PTZ) camera mounted on the exterior of a given area that can be remotely controlled to observe various angles of said area or follow objects of interest. In further embodiments, the sensors 110 may be dynamic, fitted to moving platforms or vehicles. Examples of such dynamic sensors include those mounted on unmanned aerial vehicles (drones), inspection sensors affixed to autonomous delivery robots or other vehicles in a given location, or sensors integrated into vehicles.

[0066] In an exemplary implementation, the one or more sensors 110 (e.g., image capture sensors) comprise synchronized or unsynchronized cameras with or without an overlapping field of view. For example, the synchronized or unsynchronized cameras may be configured to monitor an area of a roadway including or excluding a lane of the roadway that is divided into n-regions with each region being monitored by one or more synchronized or unsynchronized cameras. Furthermore, each of the synchronized or unsynchronized cameras can comprise a high-angle camera configured to provide a clear view of an object (e.g., a vehicle) in its field of view such that images of the object can be analyzed from an optimal perspective. In addition, the disclosed one or more sensors 110 may be configured to implement occlusion mitigation computing operations that correct, compensate, or mitigate against negative effects of undesirable objects blocking the field of view of said one or more sensors.

[0067] It is appreciated that the one or more endpoint devices 125a . . . 125n can comprise handheld computing devices, smart phones, tablets, phablets, laptop computers, desktop computers, personal digital assistants (PDAs), smart devices, wearable electronic devices, biometric devices, computer servers, virtual servers, virtual machines, and/or communication servers. In some embodiments, the one or more endpoint devices 125a . . . 125n may include a plurality of computing devices configured to communicate with one another and/or receive/transmit data communications from/to the analytics server system 105 and/or the intelligence server 138. In some instances, one or more alert data communications (e.g., the aforementioned alerts) associated with executing one or more of the disclosed methods may be visualized on one or more display devices of the one or more endpoint devices 125a . . . 125n. In some cases, the one or more endpoint devices comprise a first endpoint device that is associated with the analytics server system 105. This first endpoint device, for example, can comprise an admin endpoint device configured to monitor, control, or otherwise regulate or validate various computing operations implemented in the network system 100. Furthermore, the one or more endpoint devices 125a . . . 125n can include a second endpoint device that may be dependent or independent from the analytics server system 105. This second endpoint point device, for example, may be associated with a security or monitoring system configured to support or implement security operations or vehicular traffic management based on analyzing captured image data. It is also appreciated that the one or more endpoint devices 125a . . . 125n may include and/or are associated with the one or more sensors 110.

[0068] The output system 120 of FIG. 1, is adaptable to interface with, and drive one or more sensor interfaces using data or control logic that may be either dependent on or independent of the computing operations performed by the disclosed methods and systems. For example, the output system 120 may incorporate a visual subsystem, such as a graphical display device including but not limited to liquid crystal displays (LCDs), light-emitting diode (LED) displays, organic light-emitting diode (OLED) displays, or touchscreen interfaces, configured to render data alerts, notifications, graphical representations, or other electronic indicia derived from or responsive to the outcomes of the disclosed computing operations. Additionally, the output system 120 may integrate an audio subsystem, exemplified by speakers, transducers, or audio amplifiers, capable of generating audio data such as tones, voice prompts, or synthesized speech in response to input data or control logic stemming from, or independent of the disclosed methods. Furthermore, the output system 120 can encompass vibration-based mechanisms, such as eccentric rotating mass (ERM) motors or linear resonant actuators (LRAs), embedded within various apparatuses including automotive steering wheels, handheld mobile computing devices, wearable electronic watches, or tablet computing devices, to deliver tactile alerts or feedback patterns triggered by processed data or logic resulting from one or more of the disclosed methods.

[0069] In some embodiments, the output system 120 is configured to activate a haptic feedback subsystem, employing vibration sensors, piezoelectric elements, or similar haptic actuators to provide nuanced tactile sensations, such as variable intensity vibrations or directional cues, in response to data or control logic generated post-execution of one or more of disclosed methods. Moreover, the output system 120 may include embedded control logic designed to regulate environmental conditioning systems, thereby modulating parameters like ambient temperature via heating or cooling elements, atmospheric pressure through pressurization valves or compressors, or humidity levels using humidifiers, dehumidifiers, or evaporative systems, all in accordance with the computational results or directives associated with the disclosed methods and systems. Additionally, the output system 120 can incorporate control logic for operating a radio transmitter, facilitating the transmission of electrical or electromagnetic data—such as processed sensor readings, analytical insights, or command signals—to key interconnected systems depicted in FIG. 1, including but not limited to the analytics server 105, the intelligence server 138, and the endpoint devices 125a . . . 125n via protocols such as Wi-Fi, Bluetooth, cellular networks, or other wireless and/or wired communication standards.

[0070] In some embodiments, the output system 120 may be comprised in, or associated with one or more of the analytics server 105, the intelligence server 138, and the endpoint devices 125a . . . 125n. In some cases the output system 120 is proximal to one or more of analytics server 105, the intelligence server 138, and the endpoint devices 125a . . . 125n. For example, the output system may be comprised in, for example, a vehicle associated with the analytics server 105, the intelligence server 138, and the endpoint devices 125a . . . 125n.

System Environment

[0071] FIGS. 2 and 3 illustrate exemplary system and functional diagrams of a computing environment 200, within which one or more systems shown in FIG. 1 can be implemented. Specifically, FIG. 2 provides a system diagram of the computing environment, whereas FIG. 3 provides a detailed functional diagram of the computing environment 200.

[0072] The computing environment 200 may include a processing unit 202, a memory unit 204, an I/O unit 206, and a communication unit 208. The processing unit 202, the memory unit 204, the I/O unit 206, and the communication unit 208 may include one or more subunits for performing operations described in this disclosure. Additionally, each unit and/or subunit may be operatively and/or otherwise communicatively coupled with each other and to the network 115 of FIG. 1. The computing environment 200 may be implemented on general-purpose hardware and/or specifically-purposed hardware as the case may require.

[0073] The processing unit 202 may control one or more of the memory unit(s) 204, the I/O unit 206, and the communication unit 208 of the computing environment 200, as well as any included subunits, elements, components, devices, and/or functions performed by the memory unit 204, I/O unit 206, and the communication unit 208. The described sub-elements of the computing environment 200 may also be included in similar fashion in any of the other units and/or devices included in the network system 100 of FIG. 1. Additionally, any actions described as being performed by a processor may be executed by the processing unit 202 of FIG. 2 alone and/or by the processing unit 202 in conjunction with one or more additional processors, units, subunits, elements, components, devices, and/or the like. For example, the processing unit 202 may be configured to implement computing instructions that process or otherwise analyze received image, video, or other data from the one or more sensors discussed above.

[0074] Further, while one processing unit 202 may be shown in FIG. 2, multiple processing units may be present and/or otherwise included in the computing environment 200 or elsewhere in the network system 100 of FIG. 1. Thus, while instructions may be described as being executed by the processing unit 202 (and/or various subunits of the processing unit 202), the instructions may be executed simultaneously, serially, and/or otherwise by one or multiple processing units 202 on one or more computing devices.

[0075] In some embodiments, the processing unit 202 may be implemented as one or more computer processing unit (CPU) chips and/or graphical processing unit (GPU) chips and may include a hardware device capable of executing computer instructions. The processing unit 202 may execute instructions, codes, computer programs, and/or scripts. The instructions, codes, computer programs, and/or scripts may be received from and/or stored in the memory unit 204, the I/O unit 206, the communication unit 208, subunits, and/or elements of the aforementioned units, other devices, and/or computing environments, and/or the like.

[0076] In some embodiments, the processing unit 202 may include, among other elements, subunits such as a content management unit 212, a location determination unit 214, a graphical processing unit (GPU) 216, and a resource allocation unit 218. Each of the aforementioned subunits of the processing unit 202 may be communicatively and/or otherwise operably coupled with each other.

[0077] The content management unit 212 may facilitate generation, modification, analysis, transmission, and/or presentation of content. Content may be file content, media content, image content, video content, textual content, audio-visual content, or any combination thereof. In some instances, content on which the content management unit 212 may operate includes device information, user interface data, images, text, themes, audio files, video files, documents, and/or the like. Additionally, the content management unit 212 may control (e.g., format) the audio-visual environment and/or appearance of application data during execution of various processes. In some embodiments, the content management unit 212 may interface with a third-party content server and/or memory location for execution of its operations.

[0078] The location determination unit 214 may facilitate detection, generation, modification, analysis, transmission, and/or presentation of location information. Location information may include global positioning system (GPS) coordinates, an Internet protocol (IP) address, a media access control (MAC) address, geolocation information, a port number, a server number, a proxy name and/or number, device information (e.g., a serial number), an address, a zip code, and/or the like. In some embodiments, the location determination unit 214 may include various sensors, radar, and/or other specifically-purposed hardware elements for the location determination unit 214 to acquire, measure, and/or otherwise transform location information. The location determination unit 214 may be associated with the one or more sensors 110, discussed above.

[0079] The GPU 216 may facilitate generation, modification, analysis, processing, transmission, and/or presentation of content described above, as well as any data such as pipeline data or analysis data or other output data generated using at least a file and/or the like. In some embodiments, the GPU 216 may be used to render content for presentation on a computing device via, for example, a graphical display device or a graphical user interface (GUI) of a display device of the endpoint device 125a. The GPU 216 may also include multiple GPUs and therefore may be configured to perform and/or execute multiple processes in parallel.

[0080] The resource allocation unit 218 may facilitate the determination, monitoring, analysis, and/or allocation of computing resources throughout the computing environment 200 and/or other computing environments. For example, the computing environment may facilitate a high volume of data to be processed and analyzed. As such, computing resources of the computing environment 200 used by the processing unit 202, the memory unit 204, the I/O unit 206, and/or the communication unit 208 (and/or any subunit of the aforementioned units) such as processing power, data storage space, network bandwidth, and/or the like may be in high demand at various times during operation. Accordingly, the resource allocation unit 218 may include sensors and/or other specially-purposed hardware for monitoring performance of each unit and/or subunit of the computing environment 200, as well as hardware for responding to the computing resource needs of each unit and/or subunit. In some embodiments, the resource allocation unit 218 may use computing resources of a second computing environment separate and distinct from the computing environment 200 to facilitate a desired operation. For example, the resource allocation unit 218 may determine a number of simultaneous computing processes and/or requests. The resource allocation unit 218 may also determine that the number of simultaneous computing processes and/or requests meet and/or exceed a predetermined threshold value. Based on this determination, the resource allocation unit 218 may determine an amount of additional computing resources (e.g., processing power, storage space of a particular non-transitory computer-readable memory medium, network bandwidth, and/or the like) required by the processing unit 202, the memory unit 204, the I/O unit 206, the communication unit 208, and/or any subunit of the aforementioned units for safe and efficient operation of the computing environment while supporting the number of simultaneous computing processes and/or requests. The resource allocation unit 218 may then retrieve, transmit, control, allocate, and/or otherwise distribute determined amount(s) of computing resources to each element (e.g., unit and/or subunit) of the computing environment 200 and/or another computing environment.

[0081] The memory unit 204 may be used for storing, recalling, receiving, transmitting, and/or accessing various files and/or data during operations within the computing environment 200. In one embodiment, the memory unit 204 stores instructions, code, and/or data that may be executed by the processing unit 202. For instance, the memory unit 204 may store code that execute operations associated with one or more units and/or one or more subunits of the computing environment 200. For example, the memory unit may store code for the processing unit 202, the I/O unit 206, the communication unit 208, and for itself. Moreover, the memory unit may store code for implementing the data unit 136 associated with the analytics server system 105. Furthermore, the memory unit 204 can also be used to store one or more computing models, relationship record(s) associated with category data determined during, for example, classification computing operations executed on a captured substantially real-time data using the one or more sensors. The category data, for example, can be linked to, or characterize features of objects imaged using the one or more sensors.

[0082] Turning back to FIGS. 2 and 3, the memory unit 204 may include various types of data storage media such as solid-state storage media, hard disk storage media, virtual storage media, and/or the like. Memory unit 204 may include dedicated hardware elements such as hard drives and/or servers, as well as software elements such as cloud-based storage drives. In some implementations, memory unit 204 may include a random-access memory (RAM) device, a dynamic random-access memory (DRAM) device, a static random-access memory (SRAM) device, flash memory, read only memory (ROM) device, and/or various forms of secondary storage. The RAM device may be used to store volatile data and/or to store instructions that may be executed by the processing unit 202. For example, the instructions stored by the RAM device may be a command, a current operating state of computing environment 200, an intended operating state of computing environment 200, and/or the like. As a further example, data stored in the RAM device of memory unit 204 may include instructions related to various methods and/or functionalities described in this disclosure. The ROM device may be a non-volatile memory device that may have a smaller memory capacity than the memory capacity of a secondary storage. The ROM device may be used to store instructions and/or data that may be read during execution of computer instructions. In some embodiments, access to both the RAM device and ROM device may be faster to access than the secondary storage.

[0083] Secondary storage may comprise one or more disk drives and/or tape drives and may be used for non-volatile storage of data or as an over-flow data storage device if the RAM device is not large enough to hold all working data. Secondary storage may be used to store programs that may be loaded into the RAM device when such programs are selected for execution. In some embodiments, the memory unit 204 includes one or more databases 210 for storing any data described herein. For example, depending on the implementation, the one or more databases 210 may be used as the local computing database 111a or remote computing database 111b associated with the analytics server system 105. In some embodiments, the memory unit 204 and/or its subunits may be local relative to the analytics server system 105 and/or be remotely located relative to the analytics server system 105.

[0084] The memory unit 204 may include subunits such as an operating system unit 226, an application data unit 228, an application programming interface (API) unit 230, a content storage unit 232, and a cache storage unit 240. Each of the aforementioned subunits of the memory unit 204 may be communicatively and/or otherwise operably coupled with each other and other units and/or subunits of the computing environment 200. It is also noted that the memory unit 204 may include other modules, instructions, or code that facilitate the execution of the techniques described. For instance, the memory unit 204 may include the data unit 136 and the intelligence unit 140. The data unit 136 and/or intelligence unit 140 may be configured to implement one or more of the disclosed computing methods or operations. According to one embodiment, the data unit 136 and/or the intelligence unit 140 comprise one or more of a analytic data flux processor (ADFP), a cognitive data orchestration unit (CDOU), an insight generation hypervisor (IGH), a metric derivation computational fabric (MDCF), a phenomenological data synthesizer (PDS), a quantitative intelligence abstraction engine (QIAE), an adaptive data analytics core (ADAC), a contextual insight graph engine (CIGE), an empirical data transformation array (EDTA), or a heuristic data pattern extractor (HDPE).

[0085] The operating system unit 226 may facilitate deployment, storage, access, execution, and/or utilization of an operating system used by computing environment 200 and/or any other computing environment described in this disclosure. In some embodiments, operating system unit 226 may include various hardware and/or software elements that serve as a structural platform for the processing unit 202 to execute various operations described herein. Operating system unit 226 may further store various pieces of information and/or data associated with the operation of the operating system and/or computing environment 200 as a whole, such as a status of computing resources (e.g., processing power, memory availability, resource utilization, and/or the like), runtime information, modules to direct execution of operations described herein, user permissions, security credentials, and/or the like.

[0086] The application data unit 228 may facilitate deployment, storage, access, execution, and/or utilization of an application used by computing environment 200 and/or any other computing environment described herein. For example, an endpoint device (e.g., endpoint device 125a) may be required to download, install, access, and/or otherwise use a software application (e.g., web application) to facilitate performance of one or more of the disclosed computing operations. As such, application data unit 228 may store any information and/or data associated with an application. Application data unit 228 may further store various pieces of information and/or data associated with the operation of an application and/or computing environment 200 as a whole, such as a status of computing resources (e.g., processing power, memory availability, resource utilization, and/or the like), runtime information, user interfaces, modules to direct execution of operations described herein, user permissions, security credentials, and/or the like.

[0087] The API unit 230 may facilitate deployment, storage, access, execution, and/or use of information associated with APIs of computing environment 200 and/or any other computing environment described in this disclosure. For example, computing environment 200 may include one or more APIs for various devices, applications, units, subunits, elements, and/or other computing environments to communicate with each other and/or use the same data. Accordingly, API unit 230 may include API databases containing information that may be accessed and/or used by applications, units, subunits, elements, and/or operating systems of other devices and/or computing environments. In some embodiments, each API database may be associated with a customized physical circuit included in memory unit 204 and/or API unit 230. Additionally, each API database may be public and/or private, and so authentication credentials may be required to access information in an API database. In some embodiments, the API unit 230 may enable the analytics server system 105 and/or the display device of the endpoint device 125a to communicate with each other.

[0088] The content storage unit 232 may facilitate deployment, storage, access, and/or utilization of information associated with performance of the disclosed operations by computing environment 200 and/or any other computing environment described. In some embodiments, content storage unit 232 may communicate with content management unit 212 to receive and/or transmit content files (e.g., media content, image data, video data, audio-visual data, visualization data, etc.).

[0089] The cache storage unit 240 may facilitate short-term deployment, storage, access, analysis, and/or use of data. In some embodiments, cache storage unit 240 may serve as a short-term storage location for data so that the data stored in cache storage unit 240 may be accessed quickly. In some instances, cache storage unit 240 may include RAM devices and/or other storage media types for quick recall of stored data. Cache storage unit 240 may include a partitioned portion of storage media included in memory unit 204.

[0090] The I/O unit 206 may include hardware and/or software elements for the computing environment 200 to receive, transmit, and/or present information useful for performing processes described herein. For example, elements of the I/O unit 206 may be used to receive input from the analytics server system 105. As described, I/O unit 206 may include subunits such as an I/O device 242, an I/O calibration unit 244, and/or driver 246.

[0091] The I/O device 242 may facilitate receipt, transmission, processing, presentation, display, input, and/or output of information as a result of executed processes described in this disclosure. In some embodiments, the I/O device 242 may include a plurality of I/O devices. In some embodiments, I/O device 242 may include a variety of elements that enable a user to interface with computing environment 200. For example, I/O device 242 may include a keyboard, a touchscreen, a button, a sensor, a biometric scanner, a laser, a microphone, a camera, and/or another element for receiving and/or collecting input from a user. Additionally, and/or alternatively, I/O device 242 may include a display, a screen, a sensor, a vibration mechanism, a light emitting diode (LED), a speaker, a radio frequency identification (RFID) scanner, and/or another element for presenting and/or otherwise outputting data to a user. In some embodiments, the I/O device 242 may communicate with one or more elements of processing unit 202 and/or memory unit 204 to execute the disclosed operations.

[0092] The I/O calibration unit 244 may facilitate the calibration of the I/O device 242. For example, I/O calibration unit 244 may detect and/or determine one or more settings of I/O device 242 and then adjust and/or modify settings and/or format and/or optimize viewing of a graphical user interface and/or one or more visualizations associated with alerts or alert data communications. According to some embodiments, the I/O calibration unit 244 may be used to format or resolve, for example, data communications such as alerts or data reports into multidimensional data components (e.g., multi-modal data including multiple aspects such as image data, video data, textual data, etc.) and rendering the multidimensional data components on a display device.

[0093] In some embodiments, the I/O calibration unit 244 may use a driver 246 (or multiple drivers) to calibrate I/O device 242. For example, driver 246 may include software that is installed by I/O calibration unit 244 so that an element of computing environment 200 (or an element of another computing environment) may recognize and/or integrate with I/O device 242 for disclosed processes.

[0094] The communication unit 208 may facilitate establishment, maintenance, monitoring, and/or termination of data communications (e.g., control or alert data communications) between computing environment 200 and other computing environments, third party server systems, and/or the like. Communication unit 208 may also facilitate internal communications between various elements (e.g., units and/or subunits) of computing environment 200. For example, the communication unit 208 may be configured to receive substantially real-time data from one or more of the one or more sensors 110 as well as coordinate the transmission and/or storage of any attendant data such as analysis reports associated with analyzing the substantially real-time data, instructions and/or data to the one or more output system 120, and/or any alerts to stakeholders with one or more endpoint devices. In some embodiments, communication unit 208 may include a network protocol unit 248, an API gateway 250, an encryption unit 252, and/or a communication device 254. Communication unit 208 may include hardware and/or software elements.

[0095] The network protocol unit 248 may facilitate establishment, maintenance, and/or termination of a communication connection for computing environment 200 by way of a network. For example, the network protocol unit 248 may detect and/or define a communication protocol required by a particular network and/or network type. Communication protocols used by the network protocol unit 248 may include Wi-Fi protocols, Li-Fi protocols, cellular data network protocols, Bluetooth® protocols, WiMAX protocols, Ethernet protocols, powerline communication (PLC) protocols, and/or the like. In some embodiments, facilitation of communication for computing environment 200 may include transforming and/or translating data from being compatible with a first communication protocol to being compatible with a second communication protocol. In some embodiments, network protocol unit 248 may determine and/or monitor an amount of data traffic to consequently determine which particular network protocol is to be used for establishing a secure communication connection, transmitting data, and/or performing the disclosed methods and/or data visualization operations and/or other processes provided in this disclosure.

[0096] The API gateway 250 may allow other devices and/or computing environments to access API unit 230 of memory unit 204 of computing environment 200. For example, display devices associated with the various systems of FIG. 1 may access API unit 230 of computing environment 200 via API gateway 250. In some embodiments, API gateway 250 may be required to validate user credentials associated with a user of a display device prior to providing access to API unit 230 to a user. API gateway 250 may include instructions for computing environment 200 to communicate with another device and/or between elements of the computing environment 200.

[0097] The encryption unit 252 may facilitate translation, encryption, encoding, decryption, and/or decoding of information received, transmitted, and/or stored by the computing environment 200. Using encryption unit 252, each transmission of data may be encrypted, encoded, and/or translated for security reasons, and any received data may be encrypted, encoded, and/or translated prior to its processing and/or storage. In some embodiments, encryption unit 252 may generate an encryption key, an encoding key, a translation key, and/or the like, which may be transmitted along with any data content.

[0098] The communication device 254 may include a variety of hardware and/or software specifically purposed to facilitate communication for computing environment 200. In some embodiments, communication device 254 may include one or more radio transceivers, chips, analog front end (AFE) units, antennas, processing units, memory, other logic, and/or other components to implement communication protocols (wired or wireless) and related functionality for facilitating communication for computing environment 200. Additionally and/or alternatively, communication device 254 may include a modem, a modem bank, an Ethernet device such as a router or switch, a universal serial bus (USB) interface device, a serial interface, a token ring device, a fiber distributed data interface (FDDI) device, a wireless local area network (WLAN) device and/or device component, a radio transceiver device such as code division multiple access (CDMA) device, a global system for mobile communications (GSM) radio transceiver device, a universal mobile telecommunications system (UMTS) radio transceiver device, a long term evolution (LTE) radio transceiver device, a worldwide interoperability for microwave access (WiMAX) device, and/or another device used for communication purposes.

[0099] FIG. 4 shows an exemplary communicative coupling of the intelligence server 138 to one or more data sources. In the illustrated implementation, the intelligence server 138 comprises the above-referenced intelligence unit 140 that can include a data retrieval unit 404 and data transform unit 406. Data retrieval unit 404 may be configured to access, assess, interpret, request, or receive data, which may be adjusted, reformatted, or changed. For example, data retrieval unit 404 may request data from a remote source (e.g., data from the one or more sensors 110 and/or databases 111a and 111b of the analytics server system 105) using an API. Thus, the intelligence unit 140 may be configured to access, interpret, request, format, re-format, or receive input data from the one or more data sources outlined in data source(s) 402. For example, intelligence unit 140 may be configured to use data transform unit 406 to execute a re-configuration or other data changes or data transformations, such as a data dimension reduction prior to the analytics server 105 running intelligence computing operations on said transformed data. Data source(s) 402 may comprise real-time or near real-time captured data that is stored on one or more memories devices such as those discussed in association with FIGS. 2 and 3. In some embodiments, data source(s) 402 may be associated with a single entity (e.g., a single computing network) or with multiple entities (e.g., multiple computing networks). Data source(s) 402 may include one or more of training data 402a (e.g., input data to feed a machine learning model as part of one or more training processes), validation data 402b (e.g., data against which at least one processor may compare model output with, such as to determine model output quality), reference data 402c, and/or a substantially real-time video stream 402d. In some embodiments, intelligence unit 140 can be implemented using at least one computing device. For example, data from data sources 402 can be obtained through one or more I/O devices and/or network interfaces. Further, the received data may be stored (e.g., during execution of one or more operations) in a suitable storage or system memory. Intelligence unit 140 may also be configured to interact with a memory unit (e.g., memory unit 204), which may be implemented on a computing device that stores data in a storage device.

[0100] In some embodiments, intelligence unit 140 comprises an executable software module containing one or more instructions or computer-executable logic. These instructions are specifically configured to be executed by one or more processors, such as the processors discussed in association with FIGS. 2 and 3, to perform computing operations of the intelligence server 138 including coordinating with the various units of the intelligence server 138 to perform intelligent computing operations.

[0101] The intelligence server 138 may also include machine learning (ML) modeling unit 430, which may be configured to execute one or more modeling computing operations on an AI or ML model (e.g., model training, model re-configuration, model validation, model testing), such as those described in the processes described herein. For example, the ML modeling unit 430 may execute an operation to train an AI or ML model, such as adding, removing, or modifying a model parameter. Training of an AI or ML model may be supervised, semi-supervised, or unsupervised. In some embodiments, training of an AI or ML model may include multiple epochs, or passes of data (e.g., training data 402a) through an AI or ML model process (e.g., a training process). In some embodiments, different epochs may have different degrees of supervision (e.g., supervised, semi-supervised, or unsupervised).

[0102] Training data for a given AI or ML model under consideration may include input data (e.g., as described above) and/or data previously output from a model (e.g., forming recursive learning feedback). A model parameter may include one or more of a seed value, a model node, a model layer, a computing process, a function, a model connection (e.g., between other model parameters or between models), a model constraint, or any other digital component influencing the output of a model. A model connection may include or represent a relationship between model parameters and/or disparate models, which may be dependent or interdependent, hierarchical, and/or static or dynamic. The combination and configuration of the model parameters and relationships between model parameters discussed herein can be cognitively infeasible for the human mind to maintain or use. Without limiting the disclosed embodiments in any way, an ML or AI model may include millions, trillions, or even billions of model parameters.

[0103] ML modeling unit 430 may include model selector unit 432 (e.g., configured to select a model from among a plurality of models, such as based on input data), parameter selector unit 434 (e.g., configured to add, remove, and/or change one or more parameters of a model), and/or model generation unit 436 (e.g., configured to generate one or more machine learning models, such as according to model input data, model output data, comparison data, and/or validation data). In some embodiments, model selector unit 432 may be configured to receive input and/or transmit output to AI/ML computing processes database 490. AI/ML computing processes database 490 may store one or more AI/ML models, any of which may be fully trained, partially trained, or untrained. In one embodiment, an AI/ML model may be, or include, without limitation, one or more of (e.g., such as in the case of a metamodel) a statistical model, a complex computing process, a neural network (NN), a convolutional neural network (CNN), a generative neural network (GNN), a Word2Vec model, a bag of words model, a term frequency-inverse document frequency model, a GPT (Generative Pre-trained Transformer) model (or other autoregressive model), a Proximal Policy Optimization (PPO) model, a nearest neighbor model (e.g., k nearest neighbor model), a linear regression model, a k-means clustering model, a Q-Learning model, a Temporal Difference (TD) model, a Deep Adversarial Network model, a You Only Look Once (YOLO) computing model. A YOLO model, for example, can be used to efficiently detect and bound an object within the image data using, for example, a bounding box. Further, each of the bounding boxes for a detected object may comprise minimum coordinates and maximum coordinates. The minimum and maximum coordinates can represent the coordinates of two diagonally opposite corners of a given bounding box. In one embodiment, the minimum coordinates can correspond to the top-left corner of the bounding box, while the maximum coordinates correspond to the bottom-right corner of the bounding box. Furthermore, the intelligence server 138 may be configured to measure the relative angle between the two bounding boxes associated with a given image data and thereby track the movement of the vehicles on a roadway. The relative angle may be generated using a straight line connecting either the minimum coordinates or the maximum coordinates of the two bounding boxes.

[0104] By processing images in real-time using the disclosed models, the disclosed system becomes a high-accuracy vehicle detection system, making it suitable for applications such as real-time traffic monitoring. Other intelligent computing processes such as SSD (Single Shot MultiBox Detector) computing processes, Faster R-CNN computing processes, RetinaNet computing processes, and the like can be employed for the vehicles detection and bounding, to efficiently track the movement of the vehicles on the roadway.

[0105] Intelligence server 138 can further include predictive output generation unit 440, output validation unit 450 (e.g., configured to apply validation data to machine learning model output), feedback unit 470 (e.g., configured to apply feedback to a model), and model refinement unit 460 (e.g., configured to update or re-configure a model). In some embodiments, feedback unit 470 may receive input and/or transmit output (e.g., output from a trained, partially trained, or untrained model) to outcome metrics database 480. Outcome metrics database 480 may be configured to store output from one or more models and may also be configured to associate output with one or more models. In some embodiments, outcome metrics database 480, or other device (e.g., model refinement unit 460 or feedback unit 470) may be configured to correlate output, detect trends in output data, and/or infer a change to input or model parameters to cause a particular model output or type of model output. In some embodiments, model refinement unit 460 may receive output from predictive output generation unit 440 or output validation unit 450. In some embodiments, model refinement unit 460 may transmit the received output to featurization unit 420 or ML modeling unit 430 in one or more iterative cycles.

[0106] Any or each unit of intelligence server 138 may comprise a computing unit or a data unit, which may be a packaged functional hardware unit designed for use with other components or a part of a program that implements specific computing operations. Any or each computing unit may be implemented using a computing environment such as those discussed in conjunction with FIGS. 2 and 3. In some embodiments, the functionality of intelligence server 138 may be split across multiple computing environments to allow for distributed processing of the data, which may improve output speed and reduce computational load on individual computing environments. In some embodiments, intelligence server 138 may use load-balancing to maintain stable resource loads (e.g., processing load, memory load, or bandwidth load) across multiple computing environments and to reduce the risk of a computing environment from becoming overloaded. In these or other embodiments, the different components may communicate over one or more I/O devices and/or network interfaces.

[0107] It is appreciated that the intelligence server 138 can beneficially facilitate implementing one or more computing operations associated with the network system 100 of FIG. 1. In effect, the intelligence server 138 provides the computational backbone for complex, single, or multi-stage intelligence computing processes. It is appreciated that the intelligence server 138 can ingest a substantially real-time data from multiple sensors which can be used in an object detection, and/or establishing data indicative of proximity and contact dynamics assessment between two objects (e.g., two vehicles), where an AI model of the intelligence server 138, identifies and draws a bounding box (e.g., a structure that can surround an object in an image) around each object (e.g., vehicles) comprised in the captured substantially real-time data. Following detection, the intelligence server 138 may perform various computing operations to classify consistent identifiers to each identified object in the substantially real-time data across successive image frames within a single view of one or more sensors 110. This addresses challenges like temporary occlusion, where an object is briefly hidden from view. The intelligence server 138's processing power and architecture enables these computationally intensive tasks to happen quickly and accurately, allowing for the classification and identification of objects on a roadway.

[0108] In some embodiments, the intelligence server 138 can analyze the spatial and temporal (e.g., spatiotemporal) relationships between an identified object (e.g., a vehicle) and other objects comprised in the data components of the substantially real-time data. This can involve a deeper layer of AI and/or ML computing operations to infer the nature of the object relationships in the image data. For example, if the intelligence server 138 detects a data object near an object like “a door,” “a car,” “a house,” “a tree,” etc., it might use a trained model of the intelligence server 138 to recognize an action of a given data object such as “opening the door,” “entering the car,” “leaving a house,” “climbing the tree,” etc. Thus, the intelligence server 138 is not only configured for detecting just data objects. Rather, the intelligence server 138 can be implemented to substantially determine a complete semantic description of an interaction between a given data object and other data objects (e.g., data objects comprising pixel representations of organic or inorganic objects in the real world). This allows the intelligence server 138 to recognize and classify a wide range of actions, providing a nuanced understanding of a camera scene comprised in a substantially real-time video stream by identifying not just what is present in the scene, but also what is happening within the scene. It is appreciated that a first data object, a second data object, a third data object, etc., comprised in the substantially real-time video stream may be classified into first category data, second category data, third category data, etc., by the intelligence server 138.

Embodiments

[0109] The disclosed methods and systems relate to distraction mitigation systems, driver engagement systems, and/or driver monitoring systems. In some embodiments, the disclosed methods and systems utilize a multimodal intelligence agentic system designed for adaptive intervention in cases of driver inattentiveness or distraction in vehicles equipped with advanced driver-assistance systems. Driver inattentiveness and distraction remain leading contributors to road accidents worldwide. According to other embodiments, inattentiveness comprises a passive loss of focus that occurs when a driver's attention drifts away from the driving task without any external stimulus (e.g., due to fatigue, drowsiness, mind-wandering, etc.). In yet other embodiments, distraction comprises an active diversion of attention caused by external stimuli (e.g., making a phone call, conversing with a passenger, interacting with in-vehicle systems, etc.).

[0110] According to one embodiment, the disclosed methods and systems introduce a multimodal agentic system designed to deliver adaptive intervention strategies that mitigate driver distraction and effectively maintain engagement. In another embodiment, the disclosed methods and systems comprise an intelligence agent capable of autonomous reasoning, context-aware decision-making, and/or adaptive intervention planning. According to yet another embodiment, the disclosed methods and systems process multimodal in-cabin (e.g., in vehicle) signals to evaluate a driver's physical, cognitive, and/or emotional states in real time (e.g., substantially real time, near real time, etc.). In still another embodiment, the disclosed methods and systems considers dynamic cabin conditions (e.g., passenger presence, environmental noise, lighting, vehicle motion context, etc.).

[0111] Upon detecting signs of distraction or inattentiveness, according to another embodiment, the disclosed methods and systems autonomously formulate and execute an optimal intervention strategy tailored to at least one of: a current state associated with a driver (e.g., operator), historical behavior patterns, preferences, or in-cabin context (e.g., noise level, lighting, ongoing interactions, passenger presence, driving context (e.g., speed, traffic, weather, etc.), level of risk, etc.). In some cases, the intelligence agent selects the most appropriate modality to deliver an intervention (e.g., based on the type of distraction, based on the criticality of the situation, etc.). The intervention, in other cases, may comprise at least one of: subtle cues (e.g., lighting or sound adjustments), personalized messages, interactive conversations through intelligent voice assistants, external communications (e.g., an emergency call, reaching out to another vehicle, etc.) or physiological feedback mechanisms (e.g., seat vibration, haptic alerts, etc.).

[0112] It is appreciated that the disclosed methods and systems may adjust and/or refine (e.g., continuously, at least partially continuously, etc.) the intervention (e.g., based on the driver's responsiveness, feedback, the updated in-cabin context, etc.). It is further appreciated that, if inattentiveness persists, the disclosed methods and systems may dynamically (e.g., at least partially dynamically) escalate the intervention. In some embodiments, escalating the intervention may comprise triggering an advanced driver-assistance system, initiating a safe-stop protocol, etc.). By leveraging reinforcement learning, the system personalizes its strategies over time to align with the driver's preferences and responsiveness. This results in a highly adaptive, personalized, and context-aware system that significantly enhances road safety, driver engagement, and the overall in-cabin experience.

[0113] In some embodiments, the disclosed methods and systems may comprise at least one of the following features:

• • [0114] Integrated multimodal sensing (e.g., to enable a robust understanding of driver and cabin states)
  • [0115] Intelligent agentic reasoning to plan an intervention dynamically based on the situation
  • [0116] Context-awareness (e.g., to enable accounting for environment and driving conditions)
  • [0117] A toolbox of intervention modalities (e.g., personalized alert messages, interactive conversations through intelligent voice assistants, etc.) to support diverse interventions
  • [0118] Personalized interventions to align with driver preferences (e.g., preferences for modality, tone, delivery, etc. of interventions)
  • [0119] Modifying (e.g., continuously modifying) interventions based on driver responsiveness
  • [0120] Controls associated with when and how to escalate interventions (e.g., adjusting the level of intensity in a graduated and context-sensitive manner)
  • [0121] Learning over time from past experiences to improve performance (e.g., using reinforcement learning)
  • [0122] Seamless integration with advanced driver-assistance systems and/or autonomous driving systems
  • [0123] Seamless integration with intelligent voice assistants (e.g., Siri and Alexa)

[0124] In other embodiments, the disclosed methods and systems may comprise at least one of the following advantages:

• • [0125] Reduces accidents caused by distraction and fatigue
  • [0126] Avoids alert fatigue (e.g., by replacing static thresholds, by replacing fixed alerts, by avoiding repetitive and/or generic warnings, etc.)
  • [0127] Combines driver personalization, contextual adaptation, and/or intervention escalation into a unified in-cabin safety solution
  • [0128] Improves driver trust and/or system acceptance
  • [0129] Provides a scalable framework for next-generation human-intelligent co-pilots.

[0130] The disclosed methods and systems may comprise at least one of: a comprehensive network of in-cabin multimodal sensors, a multimodal intelligence real-time perception engine, an intelligence agent with memory, a toolset (e.g., a configurable set of intervention and engagement tools), or guardrails (e.g., dynamic guardrails) for security and/or compliance. In yet other cases, the comprehensive network of in-cabin multimodal sensors is configured to capture diverse data streams (e.g., visual signals, auditory signals, physiological signals, environmental signals, etc.).

[0131] According to one embodiment, the multimodal intelligence real-time perception engine estimates the driver's cognitive state and/or emotional state. In another embodiment, the multimodal intelligence real-time perception engine recognizes in-cabin events and/or contextual scenarios. According to yet another embodiment, the multimodal intelligence real-time perception engine comprises a set (e.g., an unlimited set) of intelligence-based expert models (e.g., face recognition for driver identification, eye gaze tracking to monitor driver visual attention, action recognition to detect activities (e.g., phone use, eating, etc.), scene understanding to interpret overall cabin context and/or to detect a presence and behavior of passengers, pets, and/or objects, etc.). It is appreciated that, by fusing outputs from the intelligence-based expert models, the multimodal intelligence real-time perception engine may deliver a holistic understanding of both the driver's internal state and the surrounding environment (e.g., to support robust and context-sensitive intervention planning).

[0132] In some embodiments, the intelligence agent comprises and/or incorporates and/or is in communication with a memory system that tracks (e.g., continuously tracks) at least one of: driver history, preferences, responsiveness. It is appreciated that the memory system may utilize identity linkage established through facial recognition. According to other embodiments, the toolset enables the intelligence agent to select and/or combine multiple actions (e.g., voice dialogues, haptic alerts, lighting adjustments, advanced driver-assistance systems-linked safety measures, etc.) to plan intervention strategies. In yet other embodiments, the guardrails ensure that only authorized and/or appropriate actions are executed within the system.

[0133] In some cases, the intelligence agent initiates and/or generates intervention and escalation delivery. It is appreciated that the intervention and escalation delivery may comprise personalized and/or adaptive engagement strategies. In one embodiment, intervention and escalation delivery comprises execution of an intervention (e.g., translating planned intervention strategies into real-time actions within the vehicle cabin). In another embodiment, intervention and escalation delivery comprises transmitting an intervention to an external system for execution.

[0134] In other cases, the disclosed systems and methods may leverage urban intelligence technologies configured to utilize sensor data (e.g., multi-sensor data, real-time data, etc.) from driving environments. According to some embodiments, an urban intelligence system may use data described herein for computing operations comprising data modeling, adaptive monitoring, data fusion, distraction detection, etc. In other embodiments, the disclosed methods may be supported by an urban intelligence computing system configured to acquire, fuse, and analyze multimodal data streams, including visual, spatial, geolocation, and contextual data from sensors deployed in physical environments (e.g., in-cabin sensors, roadway sensors, etc.). It is further appreciated that the disclosed methods and systems may be incorporated into an urban intelligence AI-driven architecture configured to generate and execute context-aware interventions.

Exemplary Workflow

[0135] FIGS. 5A, 5B, and 5C illustrate an exemplary workflow for generating and executing an intervention based on sensor data associated with a vehicle. The various blocks of FIGS. 5A, 5B, and 5C may be executed in a different order from that shown in FIGS. 5A, 5B, and 5C. Some blocks may be optional. It is appreciated that one or more data engines stored in one or more memory devices may cause one or more computing device processors to execute the various processing stages of the workflow of FIGS. 5A, 5B, and 5C.

[0136] In one embodiment, one or more data units, comprising control logic or applications stored in a non-transitory memory device, can enable the implementation of various processing stages within the workflow of FIGS. 5A, 5B, and 5C. These data units can operate independently or in conjunction with a data communication software tool for transmitting and/or receiving data, such as real-time, substantially real-time, or near real-time data. Furthermore, the one or more data units may comprise or be in communication with one or more of the above-referenced data unit 136 and/or intelligence unit 140, which collectively facilitate data handling and intelligent analysis. In one embodiment, sensor data may comprise static or dynamic video data that is processed or otherwise analyzed to determine a state associated with an operator (e.g., driver) of a vehicle, thereby supporting applications in mitigating driver distraction and driver inattentiveness.

[0137] At block 502 of FIG. 5A, the method comprises receiving, using one or more computing device processors, from one or more multimodal sensors, at a first time, first sensor data, wherein the one or more multimodal sensors are associated with or comprised in at least one of: a vehicle or an operator of the vehicle. In some embodiments, the vehicle comprises a car, a truck, a motorbike, a delivery vehicle, etc. In other cases, the first sensor data comprises at least one of: visual data, cognitive data, emotional data, social data, auditory data, physiological data, contextual data, activity data, or environmental data. In yet other cases, the one or more multimodal sensors may comprise at least one of: cameras, touch sensors, visual sensors, audio sensors, acoustic sensors, temperature sensors, motion detectors, watch (e.g., smart watch) sensors, light detection and ranging (LiDAR) sensors, etc. It is appreciated that the first sensor data may be received in real-time, substantially real-time, and/or near real-time. It is also appreciated that the first sensor data may comprise information associated with a physical environment (e.g., inside a vehicle, around a vehicle, outside a vehicle, in the vicinity of a vehicle, etc.).

[0138] At block 504, the method comprises identifying, using the one or more computing device processors, the operator of the vehicle. In some embodiments, the identifying the operator of the vehicle comprises utilizing a facial recognition system. In other embodiments, the identifying the operator of the vehicle comprises utilizing an intelligence model. According to one embodiment, a profile associated with the operator of the vehicle may be generated and/or modified (e.g., each operator of the vehicle may have their own profile). It is appreciated that the profile associated with the operator of the vehicle may comprise at least one of: preferences and/or authorizations and/or restrictions associated with interventions, feedback and/or responses associated with previous interventions, general personal data (e.g., health issues, height, driving record, etc.) associated with the operator of the vehicle, etc.

[0139] At block 506, the method comprises accessing, using the one or more computing device processors, a first database comprising historical data associated with at least one of: the vehicle or the operator of the vehicle. In one embodiment, the historical data comprises at least one of: vehicle data, contextual data, operator history data, identity data, operator preference data, operator responsiveness data, operator feedback data, or environmental data. In another embodiment, the historical data associated with at least one of: the vehicle or the operator of the vehicle comprises at least one of: at least one preference associated with operator of the vehicle, feedback previously received from the operator of the vehicle (e.g., feedback based on previous interventions), at least one response previously received from the operator of the vehicle, at least one setting associated with the operator of the vehicle, at least one condition associated with the vehicle (e.g., broken sound system, low volume level, broken air conditioning unit, etc.) or personal data (e.g., a profile) associated with the operator of the vehicle.

[0140] At block 508, the method comprises initiating first analyzing, using the one or more computing device processors, based on first accessing at least one intelligence model, the first sensor data. In one embodiment, the at least one intelligence model accesses or is in communication with the first database comprising the historical data associated with at least one of: the vehicle or the operator of the vehicle. According to another embodiment, the first accessing the at least one intelligence model comprises transmitting the first sensor data to a first computing system associated with the at least one intelligence model. In yet another embodiment, the one or more intelligence units 140 comprise the at least one intelligence model. According to still another embodiment, the at least one intelligence model comprises at least one external intelligence model. In some cases, that at least one intelligence model may comprise an AI agent.

[0141] At block 510, the method comprises determining, using the one or more computing device processors, based on the first analyzing, based on the first accessing the at least one intelligence model, the first sensor data, a first context associated with the operator of the vehicle. It is appreciated that, in some embodiments, the first context associated with the operator of the vehicle comprises or is associated with an inattentiveness of the operator of the vehicle, a distraction affecting the operator of the vehicle, or a medical ailment affecting the operator of the vehicle. In other embodiments, the first context associated with the operator of the vehicle comprises or is associated with at least one of: a first activity (e.g., eating, making a phone call, texting, etc.) associated with the operator of the vehicle, a physical state (e.g., shortness of breath, rapid heart rate, an indication of pain, rapid blinking, etc.) associated with the operator of the vehicle, or a cognitive state (e.g., crying, upset, angry, sleepy, etc.) associated with the operator of the vehicle.

[0142] According to one embodiment, the first analyzing, based on the first accessing the at least one intelligence model, the first sensor data comprises: determining, using the one or more computing device processors, a first activity (e.g., talking on the phone, texting, eating, etc.) associated with the operator of the vehicle; and determining, using the one or more computing device processors, based on the first activity associated with the operator of the vehicle, the first context associated with the operator of the vehicle.

[0143] At block 512, the method comprises determining, using the one or more computing device processors, based on the first analyzing, based on the first accessing the at least one intelligence model, the first sensor data, a second context associated with the vehicle. In one embodiment, the first analyzing, based on the first accessing the at least one intelligence model, the first sensor data comprises: determining, using the one or more computing device processors, a first passenger in the vehicle and/or a first behavior associated with the first passenger in the vehicle; and determining, using the one or more computing device processors, based on the first passenger in the vehicle or the first behavior associated with the first passenger in the vehicle, the second context associated with the vehicle.

[0144] In another embodiment, the second context associated with the vehicle comprises or is associated with at least one of: a first passenger in the vehicle, a first behavior (e.g., screaming, talking, crying, etc.) associated with the first passenger in the vehicle, a speed associated with the vehicle, lighting (e.g., flashing lights, bright lights, etc.) within the vehicle, or noise levels (e.g., shouting) within the vehicle. In yet another embodiment, the second context associated with the vehicle comprises at least one of: environmental conditions, external conditions, road conditions (e.g., narrow lanes, bumpy roads, windy roads, etc.), weather conditions (e.g., rainy, stormy, windy, sunny, etc.), a time of day (e.g., late night, early morning, during rush hour, after sunset, before sunrise, etc.), or at least one additional vehicle in a vicinity of the vehicle (e.g., a second vehicle in front of the vehicle, a second vehicle behind (e.g., tailgating) the vehicle, a second vehicle to the left or right (e.g., at least partially to the side) of the vehicle, etc.).

[0145] Turning to FIG. 5B, at block 514, the method comprises determining, using the one or more computing device processors, based on the first context associated with the operator of the vehicle and the second context associated with the vehicle, first severity data. According to yet another embodiment, the first severity data comprises a level of severity (e.g., low priority, medium priority, high priority, emergency, etc.), a severity indicator (e.g., a value on a scale), or a severity tag associated with at least one of: the first context associated with the operator of the vehicle or the second context associated with the vehicle. In some cases, the first severity data comprises a value (e.g., a first value comprised in a set of specific data values, a discrete value, a first value within a range of values, etc.). In other cases, the first severity data comprises a category (e.g., severe, minor, major, etc.). In yet other cases, the first severity data is associated with at least one of: a state (e.g., a distracted state, an inattentive state, etc.) associated with the operator of the vehicle or an indicator based on the first context associated with the operator of the vehicle and/or the second context associated with the vehicle.

[0146] At block 516, the method comprises initiating generating, using the one or more computing device processors, based on second accessing the at least one intelligence model, a first intervention, wherein the first intervention is based on: the first context associated with the operator of the vehicle, the second context associated with the vehicle, the first severity data, and the historical data associated with at least one of: the vehicle or the operator of the vehicle. In some embodiments, the first intervention is determined based on at least one of: the first context associated with the operator of the vehicle, the second context associated with the vehicle, the first severity data, or the historical data associated with at least one of: the vehicle or the operator of the vehicle. According to one embodiment, the first intervention comprises a vehicle to vehicle (V2V) intervention (e.g., communicating with a second vehicle, communicating with an emergency vehicle, communicating with a nearby vehicle, etc.). In another embodiment, the first intervention comprises a vehicle to everything (V2X) intervention (e.g., making an emergency call, communicating with a nearby vehicle, communicating with an external computing system, etc.).

[0147] According to other embodiments, the first intervention is personalized for the operator and/or personalized by a company or a supervisor for the operator (e.g., a delivery company may modify intervention settings for all of their drivers). In yet other embodiments, the first intervention comprises at least one of: a lighting adjustment, a sound adjustment, an environment adjustment, a visual message, an audio message, a vibration, a haptic alert, an emergency call, a communication with a second vehicle, or seizing control of the vehicle. According to still other embodiments, the first intervention comprises at least one of: initiating a conversation with the operator of the vehicle, adjusting a volume level in the vehicle, adjusting a temperature setting in the vehicle, utilizing an audio assistant associated with the vehicle, triggering a vibration on a steering wheel or a seat comprised in the vehicle, initiating an emergency call, connecting with a second vehicle or a second operator associated with the second vehicle for assistance, executing a safe-stop protocol, or engaging a driver assistance system.

[0148] At block 518, the method comprises initiating execution of, using the one or more computing device processors, at a second time after the first time, the first intervention. According to one embodiment, the initiating execution of, using the one or more computing device processors, at the second time after the first time, the first intervention comprises transmitting the first intervention to a first computing system associated with the vehicle.

[0149] At block 520, the method comprises receiving, using the one or more computing device processors, from the one or more multimodal sensors, at a third time after the first time and the second time, second sensor data. According to some embodiments, the second sensor data comprises at least one of: visual data, cognitive data, emotional data, social data, auditory data, physiological data, contextual data, activity data, health data, or environmental data. It is appreciated that the second sensor data may be received in real-time, substantially real-time, and/or near real-time. It is further appreciated that the second sensor data may comprise information associated with a physical environment (e.g., inside a vehicle, around a vehicle, outside a vehicle, in the vicinity of a vehicle, etc.).

[0150] At block 522, the method comprises initiating second analyzing, using the one or more computing device processors, based on third accessing the at least one intelligence model, the second sensor data. In other embodiments, the third accessing the at least one intelligence model comprises transmitting the second sensor data to the first computing system associated with the at least one intelligence model.

[0151] Turning to FIG. 5C, at block 524, the method comprises determining, using the one or more computing device processors, based on the second analyzing, based on the third accessing the at least one intelligence model, the second sensor data, a third context associated with the operator of the vehicle. In some embodiments, the third context associated with the operator of the vehicle comprises or is associated with an attentiveness of the operator of the vehicle. In one embodiment, the third context associated with the operator of the vehicle comprises or is associated with at least one of: a first activity (e.g., eating, making a phone call, texting, etc.) associated with the operator of the vehicle, a physical state (e.g., shortness of breath, rapid heart rate, an indication of pain, rapid blinking, etc.) associated with the operator of the vehicle, or a cognitive state (e.g., crying, upset, angry, sleepy, etc.) associated with the operator of the vehicle.

[0152] According to other embodiments, the third context associated with the operator of the vehicle comprises or is associated with an inattentiveness of the operator of the vehicle, a distraction affecting the operator of the vehicle, or a medical ailment affecting the operator of the vehicle. In some embodiments, the distraction affecting the operator of the vehicle may comprise at least one passenger (e.g., a talking passenger, a screaming kid, etc.), a behavior (e.g., eating, texting, etc.), an action (e.g., the operator of the vehicle answering a phone call, etc.). According to other embodiments, the medical ailment affecting the operator of the vehicle may comprise at least one of: a heart attack, a stroke, heat stroke, a seizure, a loss of consciousness, dizziness, a migraine, etc.).

[0153] At block 526, the method comprises determining, using the one or more computing device processors, based on the second analyzing, based on the third accessing the at least one intelligence model, the second sensor data, a fourth context associated with the vehicle. In some embodiments, the fourth context associated with the vehicle comprises or is associated with at least one of: a first passenger in the vehicle, a first behavior (e.g., screaming, talking, crying, etc.) associated with the first passenger in the vehicle, a speed associated with the vehicle, lighting (e.g., flashing lights, bright lights, etc.) within the vehicle, or noise levels (e.g., shouting) within the vehicle.

[0154] In yet another embodiment, the fourth context associated with the vehicle comprises at least one of: environmental conditions, external conditions, road conditions (e.g., narrow lanes, bumpy roads, windy roads, etc.), weather conditions (e.g., rainy, stormy, windy, sunny, etc.), a time of day (e.g., late night, early morning, during rush hour, after sunset, before sunrise, etc.), or at least one additional vehicle in a vicinity of the vehicle (e.g., a second vehicle in front of the vehicle, a second vehicle behind (e.g., tailgating) the vehicle, a second vehicle to the left or right (e.g., at least partially to the side) of the vehicle, etc.).

[0155] At block 528, the method comprises determining, using the one or more computing device processors, based on the third context associated with the operator of the vehicle and the fourth context associated with the vehicle, the first intervention was successful. In some cases, the determining the first intervention was successful comprises determining the first context associated with the operator of the vehicle is different from the third context associated with the operator of the vehicle. In other cases, the determining the first intervention was successful comprises comparing the first context associated with the operator of the vehicle and the third context associated with the operator of the vehicle and/or comparing the first severity data and the second severity data.

[0156] It is appreciated that, in some embodiments, it may be determined that the first intervention was unsuccessful. Furthermore, in other embodiments, a second intervention may be generated. According to yet other embodiments, the second intervention comprises an escalation or a repetition of the first intervention (e.g., a longer and/or louder and/or more intense intervention, a louder and/or higher priority alarm or alert, a more engaging intervention, etc.). In still other embodiments, the second intervention comprises at least two interventions (e.g., a combination of interventions, an audio intervention and a haptic intervention, etc.)

[0157] At block 530, the method comprises: determining, using the one or more computing device processors, based on the third context associated with the operator of the vehicle and the fourth context associated with the vehicle, feedback associated with the first intervention for fine-tuning the at least one intelligence model. It is appreciated that the feedback associated with the first intervention may be stored in the historical data associated with at least one of: the vehicle or the operator of the vehicle (e.g., for continuously training and/or fine-tuning the at least one intelligence model). According to one embodiment, fine-tuning the at least one intelligence model may comprise improving, optimizing, further training, etc. the at least one intelligence model.

[0158] In some cases, the method further comprises initiating storage of, using the one or more computing device processors, in the first database, data associated with at least one of: the first intervention, the operator of the vehicle, the feedback associated with the first intervention, or a response from the operator of the vehicle based on the first intervention. In some embodiments, storing the data associated with at least one of: the first intervention, the operator of the vehicle, the feedback associated with the first intervention, or the response from the operator of the vehicle based on the first intervention enables the at least one intelligence model to learn more (e.g., continuously) about the operator of the vehicle and/or determine the effectiveness of the first intervention.

[0159] According to other embodiments, the method further comprises: receiving, using the one or more computing device processors, from the operator of the vehicle or a supervisor of the operator of the vehicle, a first authorization or a first restriction associated with generating interventions; and initiating storage of, using the one or more computing device processors, in the first database, the first authorization or the first restriction associated with generating interventions received from the operator of the vehicle or the supervisor of the operator of the vehicle. In yet other embodiments, the first restriction may be associated with limiting access and/or utilizing usage of a specific tool and/or action, and/or based on a specific condition and/or scenario (e.g., only intervening if a first condition is met). According to still other embodiments, the first restriction may be associated with restricting a type of intervention (e.g., restricting audio interventions, restricting visual interventions, restricting haptic interventions, restricting fusing multiple types of interventions, etc.). In further embodiments, the first authorization may be associated with authorizing and/or enabling a type of intervention (e.g., authorizing audio interventions, authorizing visual interventions, authorizing haptic interventions, authorizing fusing multiple types of interventions, etc.).

[0160] The terminology used in this disclosure is for the purpose of describing particular exemplary embodiments and is not intended to be limiting. For example, as used herein, the singular forms “a”, “an” and “the” may be intended to include plural forms as well, unless the context clearly indicates otherwise. The terms “comprises,” “comprising,” “including,” and “having,” are inclusive and therefore specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, processes, elements, components, and/or groups thereof. The described method steps, processes, and operations are not to be construed as necessarily requiring their performance in the particular order discussed or illustrated, unless specifically identified as an order of performance. It is further appreciated that additional or alternative steps may be employed according to some implementations.

[0161] Although the terms first, second, third, etc., may be used to describe various elements, components, regions, layers and/or sections, these elements, components, regions, layers and/or sections should not be limited by these terms. These terms may be used to distinguish one element, component, region, layer or section from another element, component, region, layer or section. That is, terms such as “first,” “second,” and other numerical terms, when used in this disclosure, do not imply a sequence or order unless clearly indicated by the context. In addition, the term optimal and its variants (e.g., efficient, optimally, etc.) as used in this disclosure may simply indicate improving, rather than the ultimate form of ‘perfection’ or the like.

[0162] Reference in the specification to “one implementation” or “an implementation” means that a particular feature, structure, or characteristic described in connection with the implementation is included in at least one implementation of the disclosure. The appearances of the phrase “in one implementation,” “in some implementations,” “in one instance,” “in some instances,” “in one case,” “in some cases,” “in one embodiment,” or “in some embodiments” in various places in the specification are not necessarily all referring to the same implementation or embodiment.

[0163] Finally, the above descriptions of the implementations of the present disclosure have been presented for the purposes of illustration and description. It is not intended to be exhaustive or to limit the present disclosure to the precise form disclosed. Many modifications and variations are possible in light of the above teaching. It is appreciated that the scope of the present disclosure be limited not by this detailed description, but rather by the claims of this application. The present disclosure may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. Accordingly, the above discussed subject-matter is illustrative, but not limiting, of the scope of the present disclosure, which is set forth in the following claim.