Abstract: A system for generating a virtual environment for use in a vehicle remote teleoperation system including an environmental sensor for detecting an environmental condition proximate to the host vehicle, a sensor configured to generate a sensor data representative of an environment proximate to the host vehicle, a camera configured to capture an image data representative of the environment proximate to the host vehicle, and a processor configured to determine a network condition, generate a modulated data including the image data and the sensor data in response to the environmental condition being less than an environmental threshold value and the network condition exceeding a network threshold condition and to generate the modulated data without the image data in response to at least one of the environmental condition exceeding the threshold value and the network condition not exceeding the network threshold condition.

Abstract: A system for CI/CD of AI/ML based components includes a cloud infrastructure which receives multiple new requirements from a vehicle designer or a developer, with the new requirements adapted for artificial intelligence/machine learning (AI/ML) based components of a vehicle. A dataset is provided. A metamorphic relations (MR) module receives input information from the dataset and sends MR information to the dataset. A components requirements database includes the new requirements in addition to existing requirements for the AI/ML based components. The MR module also receives components requirements data from the components requirements database and sends the MR information to the components requirements database. An AI/ML algorithm analyzes the input information from the dataset and prepares an updated component dataset.

Abstract: A system for incorporating user feedback in a large language model (LLM) based retrieval augmentation (RAG) tools includes an application for incorporating user feedback (UFA). The UFA receives an input to the LLM from a host device user, generates an LLM output as a response to the input, causes a system user to verify the LLM output and provide user feedback via a human-machine interface (HMI) of the host device, and in response to the user feedback, engages a performance tuning optimizer that modifies the LLM output by: revising domain knowledge, revising system prompts, and performing constrained regeneration of the LLM output. The performance tuning optimizer progressively reduces computational resource utilization, progressively increases computational efficiency, and progressively reduces reliance on human validators and user feedback over time, and wherein the LLM output is a command to one or more systems of the host device.

Abstract: A system for computing confidence scores for large language model (LLM) based retrieval augmentation (RAG) tools includes a host device having a controller. The controller executing programmatic control logic including an algorithm for computing confidence scores for LLM based RAG tools (CLR application). The CLR application receives an input to the LLM from a user and engages an ensemble retriever. The ensemble retriever determines a similarity between the input and predetermined data in the one or more databases. The ensemble retriever generates an output and an output confidence score. The CLR application then causes a human validator to prioritize and review the output in accordance with the output confidence score, where the output is a command to one or more systems of the host device. The system progressively reduces computational resource utilization, progressively increases computational efficiency, and progressively reduces reliance on the human validator over time.

Abstract: A diversified validation test suite application (DVTSA) for a generative AI powered tool or large language model (LLM) tool includes at least first, second, and third control logics. The first control logic receives a seed test input or user input, analyzes the seed test input or user input, and extracts key elements for variations. The second control logic performs a coverage measurement of outputs of the first control logic. The third control logic causes a human validator to evaluate outputs of the second control logic relative to predefined coverage metrics and selectively and continuously iterate to cause outputs of the second control logic to increase LLM tool input robustness and output robustness from a first level to a second level greater than the first level, in both production and pre-production LLM tool processes.

Abstract: A system for generating a virtual environment for use in a vehicle remote teleoperation system including an environmental sensor for detecting an environmental condition proximate to the host vehicle, a sensor configured to generate a sensor data representative of an environment proximate to the host vehicle, a camera configured to capture an image data representative of the environment proximate to the host vehicle, and a processor configured to determine a network condition, generate a modulated data including the image data and the sensor data in response to the environmental condition being less than an environmental threshold value and the network condition exceeding a network threshold condition and to generate the modulated data without the image data in response to at least one of the environmental condition exceeding the threshold value and the network condition not exceeding the network threshold condition.

Abstract: A system for providing situational recommendations within a vehicle includes a system controller in communication with a plurality of onboard sensors, the plurality of onboard sensors adapted to collect real-time data related to a location of the vehicle and operating conditions of the vehicle, a database in communication with the system controller adapted to store data related to past actions and data related to a location of the vehicle and operating conditions of the vehicle when such past actions occurred, the system controller including a driver specific machine learning model adapted to predict a desired action based on the real-time data related to the location and operating conditions of the vehicle and data from the database, the system controller further adapted to initiate the predicted desired action, receive input from an occupant within the vehicle, and update the driver specific machine learning model.

Abstract: A system and method for locating a trailer coupler on a trailer to assist with the coupling of the trailer coupler to a vehicle hitch on a vehicle. The system includes a human-machine interface, a vehicle camera and a trailer camera, a mobile device application (App) with access to camera and mobile network a vehicle-to-infrastructure communication network, controllers, a memory, sensors, and a trailer detection and trajectory planning application. The application includes: initializing a data stream from the vehicle and trailer camera, capturing the data stream from the vehicle camera and from the trailer camera, determining key landmarks in the data stream, combining the key landmarks, determining a coupler location, computing a joint reference trajectory using the coupler location, and providing navigation assistance in real time based on the joint reference trajectory.

Abstract: A method for using idle computing power of an electric vehicle includes receiving computing tasks while the electric vehicle is electrically connected to a charging infrastructure, accepting to perform the computing tasks while the electric vehicle is electrically connected to the charging infrastructure, and in response to accepting to perform the computing tasks, performing the computing tasks while the electric vehicle is electrically connected to the charging infrastructure.

Abstract: A system and method for locating a trailer coupler on a trailer to assist with the coupling of the trailer coupler to a vehicle hitch on a vehicle. The system includes a human-machine interface, a vehicle camera and a trailer camera, a mobile device application (App) with access to camera and mobile network a vehicle-to-infrastructure communication network, controllers, a memory, sensors, and a trailer detection and trajectory planning application. The application includes: initializing a data stream from the vehicle and trailer camera, capturing the data stream from the vehicle camera and from the trailer camera, determining key landmarks in the data stream, combining the key landmarks, determining a coupler location, computing a joint reference trajectory using the coupler location, and providing navigation assistance in real time based on the joint reference trajectory.

Abstract: A motor vehicle motion control health monitoring system includes sensors and actuators disposed on the motor vehicle. The sensors measure real-time static and dynamic telemetry data about the motor vehicle, and the actuators alter static and dynamic behavior of the motor vehicle. A control module has a processor, a memory, and input/output (I/O) ports. The processor executes program code portions stored in the memory, the program code portions include: an offline portion that collects telemetry data from the motor vehicle, performs failure analysis on the telemetry data and allocates tasks based on the failure analysis; and an online portion that analyzes the telemetry data for failures within specific sensors, actuators, or functions that utilize systems of sensors and/or actuators. The online portion mitigates deviations in the telemetry data by sending a correction to the one or more sensors, actuators, and/or functions of a motor vehicle motion control system.

Abstract: A system for providing situational recommendations within a vehicle includes a system controller in communication with a plurality of onboard sensors, the plurality of onboard sensors adapted to collect real-time data related to a location of the vehicle and operating conditions of the vehicle, a database in communication with the system controller adapted to store data related to past actions and data related to a location of the vehicle and operating conditions of the vehicle when such past actions occurred, the system controller including a driver specific machine learning model adapted to predict a desired action based on the real-time data related to the location and operating conditions of the vehicle and data from the database, the system controller further adapted to initiate the predicted desired action, receive input from an occupant within the vehicle, and update the driver specific machine learning model.

Abstract: The method for autonomous driving includes receiving vehicle data about an autonomous vehicle, creating, using the vehicle data, a multi-domain ontology data structure. The multi-domain ontology data structure includes information about tasks, objects, missions, events, and responses of the autonomous vehicle in a plurality of operational design domains. The method further includes generating use cases, requirements, and test cases for the autonomous vehicle based on the multi-domain ontology data structure. The method may include controlling the autonomous vehicle in accordance with the use cases generated using the multi-domain ontology data structure.

Abstract: A system for monitoring misbehavior of neighboring vehicles and initiating interventions within a vehicle includes a plurality of onboard vehicle sensors in communication with a system controller, the system controller adapted to identify at least one neighboring vehicle, tag the at least one neighboring vehicle with a unique identifier, monitor, with the plurality of onboard sensors, driving patterns of the at least one neighboring vehicle, classify the at least one neighboring vehicle, and initiate intervention action.

Abstract: A method in an On-Demand Autonomy system includes: monitoring for a trip change request from a plurality of platoon vehicles including a first leader vehicle (Lv) and a follower vehicle (Fv). When a trip termination request is received from a platoon vehicle, the method includes broadcasting information regarding the request to the other platoon vehicles, and monitoring for an acknowledgement of the request. When the trip termination request originated from the first Lv, the method includes electing a new Lv, computing a rendezvous point for the new Lv and the Fv, and signaling the Fv and new Lv regarding the rendezvous point. When a trip modification request is received from the Fv, the method includes broadcasting information regarding the request to the first Lv, electing a new Lv, computing a rendezvous point for the new Lv and the Fv, and signaling the Fv and new Lv regarding the rendezvous point.

Abstract: A resource sharing marketplace platform includes a back-office server including a controller adapted to run a plurality of integrated systems and a wireless communication module in communication with the controller, wherein, the resource sharing marketplace platform is adapted to receive a request from a user, collect data from a plurality of acceptable service providers, and provide a response to the user.

Abstract: A system for controlling an ego vehicle following a lead vehicle without vehicle-to-vehicle (V2V) communication includes sensors disposed on the ego vehicle and capturing ego and lead vehicle condition information. Control modules disposed in the ego vehicle each have a processor, memory, and input/output (I/O) ports. The I/O ports communicate with the one or more sensors. The memory stores control logic, and the processor executes the control logic, including a lead vehicle following (LVF) application. A first control logic receives a start input from a vehicle occupant via a human-machine interface (HMI) in communication with the I/O ports. A second control logic performs a feasibility analysis of ego and lead vehicle conditions according to feasibility conditions. A third control logic selectively initiates lead vehicle following.

Abstract: A computing system including a cooperative system architecture for cataloging, informing, sharing, and managing engineering assets within an organization includes a user management subsystem for creating a registered user profile associated with a specific user of the computing system. The registered user profile includes user metadata that provides identifying characteristics of the specific user. The computing system includes an asset management subsystem including an asset repository for recording one or more engineering assets. The asset management subsystem modifies the user metadata of the registered user profile in response to the specific user recording an engineering asset in the asset repository. The computing system includes a test management subsystem including a test repository for recording one or more test bill of materials.

Abstract: A system for CI/CD of AI/ML based components includes a cloud infrastructure which receives multiple new requirements from a vehicle designer or a developer, with the new requirements adapted for artificial intelligence/machine learning (AI/ML) based components of a vehicle. A dataset is provided. A metamorphic relations (MR) module receives input information from the dataset and sends MR information to the dataset. A components requirements database includes the new requirements in addition to existing requirements for the AI/ML based components. The MR module also receives components requirements data from the components requirements database and sends the MR information to the components requirements database. An AI/ML algorithm analyzes the input information from the dataset and prepares an updated component dataset.

Abstract: A system for determining a representative vehicle software configuration for a subset vehicles that are part of a group of vehicles. The system includes one or more processors and a memory coupled to the one or more processors, where the memory stores data comprising a database and program code that, when executed by the one or more processors, causes the system to select a module configuration that is part of a set of combined module configurations as the representative vehicle software configuration based on at least one predefined criteria.