Abstract: Examples described herein provide a method for vehicle test case generation includes receiving at least one of a transactional level input/output (I/O) model based at least in part on a feature file and textual artifacts. The method further includes incrementally building behaviors from a base scenario to multiple scenarios and identifying gaps in conditions and decisions expressions in the behaviors. The method further includes building system level behaviors and generating additional scenarios based at least in part on the system level behaviors and the gaps in conditions and decisions expression in the behaviors. The method further includes generating a behavioral model based at least in part on the base scenario, the multiple scenarios, the behaviors, and the additional scenarios. The method further includes causing at least one system of a vehicle to be tested based at least in part on a test case generated using the behavioral model.

Abstract: Examples described herein provide a method for vehicle test case generation. The method includes receiving at least one of a set of requirements and textual artifacts. The method further includes generating, using a large language model (LLM)-powered agent, a feature file based at least in part on the set of requirements. The method further includes generating, a transactional level input/output (I/O) model based at least in part on the feature file. The method further includes generating a user-specific visualization of the transactional level I/O model. The method further includes causing at least one system of a vehicle to be tested based at least in part on a test case generated using the transactional level I/O model.

Abstract: Examples described herein provide a method for vehicle test case generation. The method includes receiving multiple component feature files, multiple behavioral models, and textual artifacts, each of the multiple behavioral models corresponding to at least one of the multiple component feature files. The method further includes generating component level test cases for each of the multiple component feature files and multiple behavioral models. The method further includes generating system level test cases from the component level test cases. The method further includes causing at least one system of a vehicle to be tested using a test case selected from the group consisting of at least one of the component level test cases and at least one of the system level test cases.

Abstract: A system for providing intention-based infotainment within a vehicle includes system controller adapted to collect data related to intentions of an occupant within the vehicle, develop a request based on the data related to the intentions of the occupant within the vehicle, collect data from the plurality of onboard sensors related to the request, receive, via a wireless communication module, data from remote sources related to the request, formulate, with a large language model (LLM) in communication with the system controller, via the wireless communication module, a response, and actuate systems within the vehicle to automatically, at least one of, provide, via a human machine interface (HMI), infotainment content for the occupant within the vehicle, and control, via an automated driving assistance system (ADAS), operation of the vehicle.

Abstract: An example system for automated vehicle control requirements processing includes at least one processor configured to preprocess multiple data artifacts associated with vehicle control system requirements and including at least two different modalities, including reducing redundancy and resolving conflicts between the multiple data artifacts, determine a modality of each data artifact, create an embedding for each data artifact according to the modality of the data artifact, generate, as an output of at least one machine learning model, at least one of a message sequence chart, a finite state machine and a Gherkin use case, according to the embeddings of the multiple data artifacts, and build a unified requirements model according to the at least one of the message sequence chart, the finite state machine and the Gherkin use case, wherein the unified requirements model defines control requirements for at least one vehicle control feature.

Abstract: A vehicle includes a set of vehicle sensors in communication with a controller such that output signals of each sensor are provided to the controller. The set of sensors are configured to detect at least one vehicle condition. A human machine interface system for the vehicle includes a touchscreen display in communication with a controller. The controller includes a vehicle operator identification module and a human machine interface module. The human machine interface module is configured to cause the touchscreen display to display a human machine interface, accumulate a machine learning training data set based on user interactions with the human machine interface, train a machine learning system using the training data set, and automatically adapt the displayed human machine interface based on the trained machine learning system.

Abstract: A method includes receiving a request to create a map of a geographical area, obtaining geographical-area parameters representing characteristics of the area, subdividing, based on the geographical-area parameters, the area into a plurality of sub-regions, and obtaining, for each of a plurality of robots, corresponding robot parameters representing characteristics of the robot. For each particular sub-region, the method also includes determining, based on the geographical-area parameters, corresponding sub-region parameters representing characteristics of the particular sub-region, assigning, based on the sub-region parameters and the robot parameters, a particular robot to survey the particular sub-region, and obtaining corresponding survey data captured by the particular robot, the survey data representing a survey of the particular sub-region by the particular robot.

Abstract: Examples described herein provide a method for capturing and representing implicit knowledge to enhance artificial intelligence (AI)-assisted decision-making. The method includes capturing raw data from user interactions and extracting, from the raw data, implicit knowledge representations. The method further includes generating documented implicit knowledge by systematically documenting the implicit knowledge representations, including individual actions and decisions taken during specific tasks, in a knowledge management system. The method further includes categorizing the documented implicit knowledge to create structured knowledge representations. The method further includes integrating the structured knowledge representations into an AI system, wherein the AI system utilizes the implicit knowledge to improve quality of data inputs and enhance decision-making capabilities.

Abstract: A vehicle system includes a conversion module, a control module in communication with the conversion module, and an alert module in communication with the control module. The conversion module is configured to receive vehicle specific information and user manuals and generate a plurality of embeddings for the vehicle specific information and user manuals. The control module is configured to receive the plurality of embeddings and an input query from a user specific to a vehicle, identify vehicle information for the vehicle based on the input query, and generate a response specific to the vehicle based on the vehicle information and the plurality of embeddings in response to the input query. The alert module is configured to generate and transmit a notification including the response for the user. Other example vehicle systems and control methods are also disclosed.

Abstract: Examples described herein provide a method for mitigating known-unknown threats for an internet of things (IoT) device. The method includes receiving data from the IoT device and analyzing the data to determine whether the data indicates a potential cyber threat to the IoT device. The method further includes determining whether the potential cyber threat is a known-known cyber threat or a known-unknown cyber threat. Responsive to determining that the potential cyber threat is the known-unknown cyber threat, the method includes identifying a mitigation action associated with the known-unknown cyber threat to overcome the known-unknown cyber threat. The method further includes updating a resource sharing security matrix to include the known-unknown cyber threat and the mitigation action associated with the known-unknown cyber threat.

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 method of operating an automated design system for configuring a human-machine interface (HMI) device includes a large-language model (LLM) module of the automated design system receiving a set of HMI system development metrics, including system requirements data, system objectives data, and/or source code data, and using these HMI system development metrics to generate an interaction sequence script containing a sequence of steps for executing an HMI feature on the HMI device. A system controller configures the HMI device using the set of HMI system development metrics and then generates a feedback log containing feedback data received from test clinic contributors using the interaction sequence script to test the configured HMI device. A multimodal generative artificial intelligence (AI) module generates a set of proposed HMI changes using the feedback data. The system controller then reconfigures the HMI device using a subset of HMI changes selected from the proposed HMI changes.

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: Examples described herein provide a method for mitigating known-unknown threats for an internet of things (IoT) device. The method includes receiving data from the IoT device and analyzing the data to determine whether the data indicates a potential cyber threat to the IoT device. The method further includes determining whether the potential cyber threat is a known-known cyber threat or a known-unknown cyber threat. Responsive to determining that the potential cyber threat is the known-unknown cyber threat, the method includes identifying a mitigation action associated with the known-unknown cyber threat to overcome the known-unknown cyber threat. The method further includes updating a resource sharing security matrix to include the known-unknown cyber threat and the mitigation action associated with the known-unknown cyber threat.

Abstract: A computer-implemented method when executed by data processing hardware causes the data processing hardware to perform operations. The operations include initiating, at a first ecosystem, a request, the first ecosystem being one of a donor and a requestor, discovering, by a second ecosystem, the initiated request, the second ecosystem being the other of the donor and requestor, and executing, at one of a first ecosystem and a second ecosystem, a resource exchange auction protocol (REAP). The REAP includes controlling access to resources, assessing the other of the first ecosystem and the second ecosystem, selecting the other of the first ecosystem and the second ecosystem, and sharing the resources of one of the first ecosystem and the second ecosystem with the other of the first ecosystem and the second ecosystem.

Abstract: A context-based reminder system that determines a context-based reminder conveyed to an occupant of a vehicle includes a back-end server in wireless communication with one or more controllers that are part of the vehicle by a communication network. The back-send server receives, over the communication network, data indicative of a vehicle-oriented context and a user-oriented context from the one or more controllers of the vehicle and determines the context-based reminder based on the vehicle-oriented context and the user-oriented context. An output device of the vehicle creates a notification to the occupant indicative of the context-based reminder.

Abstract: A system for imitating target vehicle behavior in automated driving includes sensors capturing ego and target vehicle condition information, actuators selectively altering an ego vehicle state, and control modules. The control modules execute a target vehicle imitating (TVI) application. A first TVI control logic estimates a target vehicle state and trajectory. The ego and target vehicle condition information partially define the target vehicle state and trajectory. A second control logic evaluates target vehicle safety and performance constraints. A third control logic selectively initiates an imitation mode of the ego vehicle based on target and ego vehicle statuses relative to the target vehicle safety and performance constraints. A fourth control logic, models the target vehicle and optimizes a planned ego vehicle path subject to actuator constraints. A fifth control logic generates outputs to the actuators to cause the ego vehicle to follow the planned path and imitate the target vehicle.

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.