Abstract: A method and system for calculating route guidance for a personal transport device based on prosocial costs is described. In one embodiment, the method includes calculating, by a processor associated with the personal transport device, a plurality of routes from a first location to a second location and determining, by the processor, one or more prosocial factors associated with each route of the plurality of routes. The method also includes calculating, by the processor, a prosocial cost associated with each route of the plurality of routes. Based on the prosocial costs, the method further includes providing, by the processor, route guidance to a user of the personal transport device from the first location to the second location along a selected route from the plurality of routes and displaying the selected route on a display associated with the personal transport device.

Abstract: A method and system for implementing an action response in a vehicle based on predicted prosocial behavior of a user of the vehicle. In one embodiment, the method includes receiving real-time physiological and behavioral inputs from the user of the vehicle. The method also includes inputting the real-time physiological and behavioral inputs into a prosocial behavior prediction module onboard the vehicle. The method further includes using the prosocial behavior prediction module to output a prosocial behavior prediction for the user based on the real-time physiological and behavioral inputs from the user of the vehicle. In response to the output prosocial behavior prediction for the user, the method includes implementing at least one action response to a system of the vehicle.

Abstract: A system and method for providing a situational awareness based adaptive driver vehicle interface that include receiving data associated with a driving scene of an ego vehicle and eye gaze data and analyzing the driving scene and the eye gaze data and performing real time fixation detection pertaining to the driver's eye gaze behavior to determine a level of situational awareness with respect to objects that are located within the driving scene. The system and method also include determining at least one level of importance associated with each of the objects and communicating control signals to control at least one component based on at least one of: the at least one level of importance associated with each of the objects that are located within the driving scene and the level of situational awareness with respect to each of the objects that are located within the driving scene.

Abstract: A system and method for predicting a driver's situational awareness that includes receiving driving scene data associated with a driving scene of an ego vehicle and eye gaze data to track a driver's eye gaze behavior with respect to the driving scene. The system and method also include analyzing the eye gaze data and determining an eye gaze fixation value associated with each object that is located within the driving scene and analyzing the driving scene data and determining a situational awareness probability value associated with each object that is located within the driving scene that is based on a salience, effort, expectancy, and a cost value associated with each of the objects within the driving scene. The system and method further include communicating control signals to electronically control at least one component based on the situational awareness probability value and the eye gaze fixation value.

Abstract: A method and system for modifying range of a battery of a personal transport device based on prosocial behavior is described. In one embodiment, the method includes detecting at least one object located on a path on which the personal transport device is traveling and calculating a prosocial yielding behavior parameter associated with the at least one object and the personal transport device. The method also includes comparing the calculated prosocial yielding behavior parameter to a threshold value. Based on the comparison of the calculated prosocial yielding behavior parameter to the threshold value, access is provided to at least a part of a power reserve portion of a battery of the personal transport device.

Abstract: Systems and methods for determining trust across mobility platforms are provided. In one embodiment, a method includes receiving first mobility data for a first automation experience of a user with a first mobility platform. The method also includes receiving a swap indication for a second automation experience of the user with a second mobility platform after the first automation experience. The method further includes selectively assigning the first mobility platform to a first mobility category and the second mobility platform to a second mobility category different than the first mobility category. The method yet further includes calculating an estimated trust score for the second automation experience by applying a trust model based on the first mobility category, the second mobility category, and a sequence of the first automation experience and the second automation experience. The method includes modifying operation of the second mobility platform based on the estimated trust score.

Abstract: A method and system for implementing real-time feedback to a user of a personal transport device, such as an electric scooter, based on prosocial behavior is provided. In one embodiment, the method includes detecting at least one object located on a path on which the personal transport device is traveling and calculating a prosocial yielding behavior parameter associated with the at least one object and the personal transport device. The method also includes comparing the calculated prosocial yielding behavior parameter to a threshold value and, based on the comparison of the calculated prosocial yielding behavior parameter to the threshold value, providing real-time feedback to the user of the personal transport device.

Abstract: An estimation system includes a plurality of sensors that generate a multimodal signal, where the multimodal signal indicates a state of a user. The estimation system also includes at least one processor that receives the multimodal signal from the plurality of sensors, and determines a workload experienced by the user based on the multimodal signal and a workload model, wherein the workload model relates multimodal signal data to an experienced workload.

Abstract: An automated vehicle (AV) is configured to perform automated travel, and includes vehicle sensors configured to detect a second vehicle in a surrounding environment of the AV. The AV includes at least one of a brake mechanism, an accelerator mechanism, a steering control, and a user interface configured to generate a user response to automated travel by the AV. The AV includes a computing device configured to identify an interaction between the AV and the second vehicle while executing an automated travel path, and receive user responses to automated travel by the AV. The computing device is configured to determine at least one aspect of wellbeing, trust, and satisfaction of the user riding the AV based on the user responses, and determine a learned optimal policy which increases the at least one aspect of wellbeing, trust, and satisfaction based on the user responses.

Abstract: A vehicle simulation system includes a control mechanism that is actuated by a user to control a vehicle in a simulation, and a plurality of sensors that generate a multimodal signal. The multimodal signal indicates a state of the user during the simulation. The vehicle simulation system also includes at least one processor that receives the multimodal signal from the plurality of sensors, develops a workload model based on the multimodal signal using a machine learning algorithm, and determines a workload experienced by the user based on the multimodal signal and the workload model.

Abstract: A vehicle simulation system includes a control mechanism that is actuated by a user to control a vehicle in a simulation, and a plurality of sensors that generate a multimodal signal. The multimodal signal indicates a state of the user during the simulation. The vehicle simulation system also includes at least one processor that receives the multimodal signal from the plurality of sensors, develops a workload model based on the multimodal signal using a machine learning algorithm, and determines a workload experienced by the user based on the multimodal signal and the workload model.

Abstract: According to one aspect, systems, methods, and/or techniques associated with operator take-over prediction may include receiving a two-channel series of images of an operating environment through which a vehicle is travelling. A first series of images may be represented by labels corresponding to classified objects. A second series of images may be represented as a gaze heatmap or an eye tracking heatmap. Additionally, feeding encoded series of images through a three-dimensional (3D) convolutional neural network (CNN) to produce a first output, receiving sets of information corresponding to a first series of images and a second series of images in time, feeding sets of information through processing layers to produce additional outputs, concatenating the first output and the additional outputs to produce a concatenation output, and feeding the concatenation output through additional processing layers to generate an operator take-over prediction may be performed.

Abstract: A method and system for calculating route guidance for a personal transport device based on prosocial costs is described. In one embodiment, the method includes calculating, by a processor associated with the personal transport device, a plurality of routes from a first location to a second location and determining, by the processor, one or more prosocial factors associated with each route of the plurality of routes. The method also includes calculating, by the processor, a prosocial cost associated with each route of the plurality of routes. Based on the prosocial costs, the method further includes providing, by the processor, route guidance to a user of the personal transport device from the first location to the second location along a selected route from the plurality of routes and displaying the selected route on a display associated with the personal transport device.

Abstract: A method and system for implementing real-time feedback to a user of a personal transport device, such as an electric scooter, based on prosocial behavior is provided. In one embodiment, the method includes detecting at least one object located on a path on which the personal transport device is traveling and calculating a prosocial yielding behavior parameter associated with the at least one object and the personal transport device. The method also includes comparing the calculated prosocial yielding behavior parameter to a threshold value and, based on the comparison of the calculated prosocial yielding behavior parameter to the threshold value, providing real-time feedback to the user of the personal transport device.

Abstract: A method and system for modifying range of a battery of a personal transport device based on prosocial behavior is described. In one embodiment, the method includes detecting at least one object located on a path on which the personal transport device is traveling and calculating a prosocial yielding behavior parameter associated with the at least one object and the personal transport device. The method also includes comparing the calculated prosocial yielding behavior parameter to a threshold value. Based on the comparison of the calculated prosocial yielding behavior parameter to the threshold value, access is provided to at least a part of a power reserve portion of a battery of the personal transport device.

Abstract: According to one aspect, systems and techniques for adaptive trust calibration may include usage of a driving style predictor, including a memory and a processor. The memory may store one or more instructions and the processor may execute one or more of the instructions stored on the memory to perform one or more acts, actions, or steps, such as receiving a current automated vehicle (AV) driving style, receiving an indication of an event and an associated event type, receiving an indication of a driver takeover, concatenating the current AV driving style and one or more of the event type or the driver takeover to generate an input, and passing the input through a neural network, which may include a gated recurrent unit (GRU), to generate a preference change associated with the AV driving style.

Abstract: A method for predicting takeover events across mobilities may monitor responses during takeover events in a car simulation from a plurality of participants. The method may monitor responses during takeover events in a micro-mobility simulation from the plurality of participants. The method may perform statistical analysis to find patterns and deviations between characteristics extracted from the responses during the takeover events in the car simulations and characteristics extracted from responses during the takeover events in the micro-mobility simulations. The method may form takeover predictions on a different type of micro-mobility vehicle using predictive modeling from the characteristics extracted from the responses during the takeover events in the car simulations and characteristics extracted from responses during the takeover events in the micro-mobility simulations. The method may integrate transfer learning in the predictive modeling.

Abstract: A method and system for modeling trust levels of drivers and modifying autonomous systems in real time in response to predicted trust levels. These modifications may be made by one or more systems, including an autonomous driving agent. An end-to-end attention network known as a Selective Windowing Attention Network (SWAN) learns directly from time-series data and assigns attention to critical areas.

Abstract: An automated vehicle (AV) is configured to perform automated travel, and includes vehicle sensors configured to detect a second vehicle in a surrounding environment of the AV. The AV includes at least one of a brake mechanism, an accelerator mechanism, a steering control, and a user interface configured to generate a user response to automated travel by the AV. The AV includes a computing device configured to identify an interaction between the AV and the second vehicle while executing an automated travel path, and receive user responses to automated travel by the AV. The computing device is configured to determine at least one aspect of wellbeing, trust, and satisfaction of the user riding the AV based on the user responses, and determine a learned optimal policy which increases the at least one aspect of wellbeing, trust, and satisfaction based on the user responses.

Abstract: According to one aspect, an adaptive driving style system may include a set of two or more sensors, a memory, and a processor. The set of two or more sensors may receive two or more sensor signals. The memory may store one or more instructions. The processor may execute one or more of the instructions stored on the memory to perform one or more acts, actions, or steps, including training a trust model using two or more of the sensor signals as input, training a preference model using the trust model and two or more of the sensor signals as input, and generating a driving style preference based on an adaptive driving style model including the trust model and the preference model.