Abstract: Systems, methods, and other embodiments described herein relate to building the trust of an occupant in an automated vehicle function. In one embodiment, a method for buildling the trust includes acquiring trust data regarding an automated function of a vehicle, an external environment of the vehicle, and an occupant of the vehicle. The method also includes processing the trust data to determine a baseline trust of the occupant in the automated function executing an action for the vehicle. The method also includes identifying a trust level of the occupant. The method further includes determining, based, at least in part, on the trust data and in response to identifying that the trust level satisfies a threshold, a trust message and a content type and a delivery type of the trust message. The method further includes delivering the trust message to the occupant.

Abstract: Systems and methods for identifying artificial intelligence (AI) personas to optimally influence driving behavior are provided. For example, a methodology of the presently disclosed technology may comprise: (1) determining a target driving behavior for a driver of a vehicle based on driving situation; (2) identifying a persona for an AI assistant with a highest predicted probability of influencing the driver to engage in the target driving behavior; and (3) using the AI assistant with the identified persona to present information to the driver to influence the driver to engage in the target driving behavior. In certain embodiments, identifying the persona for the AI assistant with the highest predicted probability of influencing the driver to engage in the target driving behavior may comprise determining the identified persona most reduces, among a plurality of personas, an objective function.

Abstract: Systems and methods for assisting a driver using a foundation model in a shared-autonomy driving mode of a vehicle are disclosed herein. One embodiment of a shared-autonomy assistance subsystem processes, in a vehicle operating in a shared-autonomy driving mode, inputs including vehicle state information, external-road-agent state information, vehicle environmental sensor data, and map data using one or more encoder neural networks that have been trained to extract features for a large language model (LLM). The subsystem inputs the extracted features to the LLM. The subsystem predicts, using the LLM, an objective of a driver of the vehicle. The subsystem then executes, based on an output from the LLM, one or more actions to assist the driver in meeting the predicted objective. The one or more actions include controlling, at least in part, operation of the vehicle.

Abstract: Systems, methods, and other embodiments described herein relate to generating instructions using multiple models for maneuvering on a road according to an operator profile and adapting the instructions using multi-modal data. In one embodiment, a method includes generating an operator profile by a learning model using road history and an operator goal on a road during a driving scenario for a vehicle. The method also includes estimating a driving command using an automated driving system (ADS) and directions using a language model for the driving scenario. The method also includes communicating maneuvers for the road to an operator using the driving command, the directions, the operator profile, and a track profile.

Abstract: A method for a vehicle-based driving simulator is described. The method includes reading a current configuration/setting/driving mode of a vehicle. The method also includes generating a dynamic model of the vehicle based on the current configuration/setting/driving mode of the vehicle. The method further includes selecting a virtual driving scenario for the vehicle according to the current configuration/setting/driving mode of the vehicle. The method includes actuating hardware of the vehicle to simulate performance of the selected virtual driving scenario in the vehicle.

Abstract: Systems, methods, and other embodiments described herein relate to building the trust of an occupant in an automated vehicle function. In one embodiment, a method for building the trust includes acquiring trust data regarding an automated function of a vehicle, an external environment of the vehicle, and an occupant of the vehicle. The method also includes processing the trust data to determine a baseline trust of the occupant in the automated function executing an action for the vehicle. The method also includes identifying a trust level of the occupant. The method further includes determining, based, at least in part, on the trust data and in response to identifying that the trust level satisfies a threshold, a trust message and a content type and a delivery type of the trust message. The method further includes delivering the trust message to the occupant.

Abstract: Systems, methods, and other embodiments described herein relate to detecting dissonance by an operator using a learning model during shared control involving a driving scenario from an operator preference and cue for adapting a driving model. In one embodiment, a method includes detecting characteristics about a driving scenario and an operator from acquired sensor data and an operator factor. The method also includes predicting dissonance for an automated takeover using a learning model with the characteristics, a driving command, and a cue about the operator. The method also includes adapting a shared-driving model (SDM) associated with a vehicle during a maneuver using the dissonance.

Abstract: Systems, methods, and other embodiments described herein relate to training a learning model using labeled data generated through a suggestion from an assisting operator to another operator for executing a task. In one embodiment, a method includes acquiring a driving suggestion from an assisting operator associated with a driving scenario involving a vehicle. The method also includes receiving a driving command and vocal data from the vehicle about following the driving suggestion during the driving scenario. The method also includes training a shared-driving model using the driving suggestion, the driving command, and the vocal data.

Abstract: are disclosed herein. One embodiment of a virtual assistant enhancement system includes a plurality of markers installed in a vehicle in a corresponding plurality of different locations. The system activates, via a generative artificial intelligence (AI)-based virtual assistant of the vehicle, one or more of the plurality of electronic markers. The system also refers to the one or more activated electronic markers in instructions communicated to a user by the generative AI-based virtual assistant to assist the user in performing a task pertaining to the vehicle.

Abstract: In one embodiment, a computer-implemented method for driver training using zone of proximal learning (ZPL) includes receiving, by one or more processors, driving data with respect to a driver operating a vehicle, estimating, using a personal behavior model, a driver profile based on the driving data, estimating one or more zone of proximal development (ZPD) states based at least in part on the driver profile, and performing one or more vehicle actions to place the driver into the one or more ZPD states.

Abstract: A teaching curriculum method for generating teaching actions for drivers, includes obtaining driving data from a plurality of driving scenarios, the driving data comprises vehicle trajectory information and corresponding scene context information, the driving scenarios comprising instructed driving events and uninstructed driving events, encoding, with a behavior model, the driving data, wherein the encoded driving data comprises an indication that a corresponding one of the driving scenarios comprises one of the instructed driving event or the uninstructed driving event, determining, with a trajectory estimator processing the encoded driving data, one or more driving skill transitions based on a presence or an absence of the indication, and generating, with a teacher action model, a teaching action for one of the plurality of driving scenarios.

Abstract: In accordance with one embodiment, a system includes a processor, a memory module communicatively coupled to the processor, an NLP module communicatively coupled to the processor, and a set of machine-readable instructions stored in the memory module. The machine-readable instructions, when executed by the processor, direct the processor to perform operations including receiving a text data, and receiving a training text data for training one or more models of the NLP module. The operations also include generating, with a novice model of the NLP module, a novice suggestion based on the text data and the training text data to present an idea related to the text data, generating, with an expert model of the NLP module, an expert suggestion based on the text data and the training text data to present an idea elaborating on the text data, and outputting the novice suggestion and/or the expert suggestion.

Abstract: A method for machine-assisted collaborative product design is described. The method includes training a neural network to simulate a plurality of stakeholder personas in a product review process to provide a plurality of stakeholder models. The method also includes simulating, using the plurality of stakeholder models, the plurality of stakeholder personas in the product review process of a potential product. The method further includes aggregating individual scores output from the plurality of stakeholder models corresponding to each of the plurality of stakeholder personas regarding the potential product; wherein each of the individual scores corresponds to a stakeholder persona and that stakeholder persona's reaction to the potential product. The method also includes displaying a summary providing an overview of the aggregated individual scores regarding the potential product to a user.

Abstract: A system and method for generating a predicted vehicle trajectory includes a generative adversarial network configured to receive a trajectory vector of a target vehicle and generate a set of latent state vectors using the received trajectory vector and an artificial neural network. The latent state vectors each comprise a high-level sub-vector, ZH. The GAN enforces ZH to be correlated to an annotation coding representing semantic categories of vehicle trajectories. The GAN selects a subset, from the set of latent state vectors, using farthest point sampling and generates a predicted vehicle trajectory based on the selected subset of latent state vectors.

Abstract: Systems and methods for generating prototypes are disclosed. In one embodiment, a computer-implemented method of creating a prototype includes receiving one or more input design parameters, generating, using a first neural network, a plurality of prototypes based on the one or more input design parameters, generating, using a second neural network, one or more decoy prototypes, and presenting, by an electronic display, a report including at least a portion of the plurality of prototypes and at least one of the one or more decoy prototypes.

Abstract: Systems and methods for providing a design with consumer feedback are provided. The method may include receiving a design within a design environment, wherein the design comprises a plurality of attributes. The method may further include automatically generating, using a computer model, consumer-based feedback regarding at least one attribute of the plurality of attributes. The method may additionally include presenting the consumer-based feedback within the design environment in real-time.

Abstract: In accordance with one embodiment, a system includes a processor, a memory module communicatively coupled to the processor, an NLP module communicatively coupled to the processor, and a set of machine-readable instructions stored in the memory module. The machine-readable instructions, when executed by the processor, direct the processor to perform operations including receiving a text data, and receiving a training text data for training one or more models of the NLP module. The operations also include generating, with a novice model of the NLP module, a novice suggestion based on the text data and the training text data to present an idea related to the text data, generating, with an expert model of the NLP module, an expert suggestion based on the text data and the training text data to present an idea elaborating on the text data, and outputting the novice suggestion and/or the expert suggestion.

Abstract: A system and method for generating a predicted vehicle trajectory includes a generative adversarial network configured to receive a trajectory vector of a target vehicle and generate a set of latent state vectors using the received trajectory vector and an artificial neural network. The latent state vectors each comprise a high-level sub-vector, ZH. The GAN enforces ZH to be correlated to an annotation coding representing semantic categories of vehicle trajectories. The GAN selects a subset, from the set of latent state vectors, using farthest point sampling and generates a predicted vehicle trajectory based on the selected subset of latent state vectors.