Abstract: Systems, methods, and other embodiments described herein relate to integrating human decision-making into a model-based system. In one embodiment, a method includes acquiring sensor data, including driver data about a driver of a vehicle and driving data about the vehicle and a surrounding environment of the vehicle. The method includes encoding, using a world encoder, the sensor data into a latent representation. The method includes determining human decision- making characteristics according to the latent representation. The method includes generating a control signal for providing shared control of the vehicle according to the human decision-making characteristics and the latent representation.

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 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: Systems, methods, and other embodiments described herein relate to stylizing messages within a vehicle according to an occupant and a current context. In one embodiment, a method includes determining a style for presenting messages associated with an occupant of a vehicle according to a context defined in relation to an occupant and an environment of the vehicle. The method includes generating a message according to the style for the occupant. The method includes providing the message to the occupant.

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: Systems and methods are provided trajectory prediction that leverages game-theory to improve coverage of multi-modal predictions. Examples of the systems and methods include obtaining training data including first trajectories for a first plurality of agent devices and first map information of a first environment for a past time horizon and applying the training data to a game-theoretic mode-finding algorithm to generate a mode-finding model for each agent device that predicts modes of the first trajectories. A trajectory prediction model can be trained on the predicted modes as a coverage loss term between predicted modes. Future trajectories can be predicted for a second plurality of agent devices based on applying observed data to the trajectory prediction model. A control signal can then be generated to effectuate an autonomous driving command on an agent device of the second plurality of agent devices based on the predicted future trajectories.

Abstract: Systems and methods for learning and managing robot user interfaces are disclosed herein. One embodiment generates, based on input data including information about past interactions of a particular user with a robot and with existing HMIs of the robot, a latent space using one or more encoder neural networks, wherein the latent space is a reduced-dimensionality representation of learned behavior and characteristics of the particular user, and uses the latent space as input to train a decoder neural network associated with (1) a new HMI distinct from the existing HMIs or (2) a particular HMI among the existing HMIs to alter operation of the particular HMI. The trained first decoder neural network is deployed in the robot to control, at least in part, operation of the new HMI or the particular HMI in accordance with the learned behavior and characteristics of the particular user.

Abstract: A driving simulator may include a controller programmed to simulate operation of a vehicle being driven by a driver, the vehicle including assistive driving technology, receive driver data associated with the driver, determine whether the driver is distracted based on the driver data, and upon determination that the driver is distracted, simulate a particular driving event.

Abstract: Systems and methods for a powered, robotic exoskeleton, or exosuit, for a user's limbs and body are provided. The exosuit may be equipped with airbag devices mounted at various locations on the suit. The exosuit may include on-board computing equipment that can sense, compute control commands in real-time, and actuate limbs and airbags to restore stability (fall prevention) and minimize injuries due to falls, should they happen (fall protection).

Abstract: Systems and methods for learning and managing robot user interfaces are disclosed herein. One embodiment generates, based on input data including information about past interactions of a particular user with a robot and with existing HMIs of the robot, a latent space using one or more encoder neural networks, wherein the latent space is a reduced-dimensionality representation of learned behavior and characteristics of the particular user, and uses the latent space as input to train a decoder neural network associated with (1) a new HMI distinct from the existing HMIs or (2) a particular HMI among the existing HMIs to alter operation of the particular HMI. The trained first decoder neural network is deployed in the robot to control, at least in part, operation of the new HMI or the particular HMI in accordance with the learned behavior and characteristics of the particular user.

Abstract: A method performed by an autonomous vehicle includes identifying a condition preventing the autonomous vehicle from proceeding along an intended route. The method also includes prompting a passenger of the autonomous vehicle to interact with a driver of a first vehicle in response to identifying the condition. The method further includes receiving, from the passenger, an input at an interface of the autonomous vehicle indicating a successful interaction or an unsuccessful interaction with the driver. The method also includes controlling the autonomous vehicle to proceed along the intended route based on the input indicating the successful interaction with the driver.

Abstract: Disclosed are systems and methods for training at least one policy using a framework for encoding human behaviors and preferences in a driving environment. In one example, the method includes the steps of setting parameters of rewards and a Markov Decision Process (MDP) of the at least one policy that models a simulated human driver of a simulated vehicle and an adaptive human-machine interface (HMI) system configured to interact with each other and training the at least one policy to maximize a total reward based on the parameters of the rewards of the at least one policy.

Abstract: A method for certified control of a self-driving ego vehicle is described. The method includes analyzing a safety situation of the self-driving ego vehicle to determine a proposed vehicle control action using a main controller of the self-driving ego vehicle. The method also includes presenting, by the main controller, the proposed vehicle control action to an interlock controller, including a certificate of the proposed vehicle control action. The method further includes checking a safety certification evidence from the certificate by the interlock controller using a predefined safety argument to verify the safety certification evidence of the certificate. The method also includes directing, by a low-level controller, the self-driving ego vehicle to perform a certified vehicle control action.

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 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: 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: 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 personalized notification method for a mobility as a service (MaaS) vehicle includes receiving conditional personalized notification features from a passenger of the MaaS vehicle. The method also includes monitoring current driving environment of the MaaS vehicle to determine whether a condition of the conditional personalized notification features is satisfied. The method further includes notifying the passenger when the condition is satisfied via at least one localized output device in a compartment of the MaaS vehicle.

Abstract: A driver monitor system and method for predicting impairment of a user of a vehicle. The system includes video cameras, an input device for inputting a list of medications being taken by the driver. Processing circuitry predicts side effects of the medications based on the half-life of the medication, detecting eye gaze movement, eye lid position, and facial expression of the user using images from the video camera, predicting whether the user is transitioning into an impaired physical state that is a side effect of the medications, verifying the side effect of the medications, determining whether the user is fit to drive using the verified side effects of the medications, and outputting to the vehicle an instruction to operate the vehicle in a level of automation that makes up for the at least one side effect or to perform a safe pull over operation of the vehicle.