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 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: 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: 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 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: 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: 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.

Abstract: Aspects of adaptive trust calibration may include receiving a trust model for an occupant of an autonomous vehicle calculated based on occupant sensor data and a first scene context sensor data, and/or receiving a second scene context sensor data associated with an environment of the autonomous vehicle, determining an over trust scenario or an under trust scenario based on the trust model and a trust model threshold, and generating and implementing a human machine interface (HMI) action or a driving automation action based on the determination of the over trust scenario or the determination of the under trust scenario, and/or the second scene context sensor data.

Abstract: Siamese neural network (SNN) based adaptive driving style prediction may be achieved by calculating a first distance between the input data and a first class of a set of anchor data using a trained SNN, calculating a second distance between the input data and a second class of the set of anchor data using the trained SNN, and generating an adaptive driving style prediction based on the first distance and the second distance. The trained SNN may be trained based on two or more sensor signals received during a training phase, a distance-based loss for the two or more sensor signals from the training phase, and by back-propagating the distance-based loss.

Abstract: A system and method for learning temporally consistent video synthesis using fake optical flow that include receiving data associated with a source video and a target video. The system and method also include processing image-to-image translation across domains of the source video and the target video and processing a synthesized temporally consistent video based on the image-to-image translation. The system and method further include training a neural network with data that is based on synthesizing of the source video and the target video.

Abstract: Systems and methods for clustering human trust dynamics are provided. In one embodiment, a computer implemented method for clustering human trust dynamics is provided. The computer implemented method includes receiving trust data for a plurality of participants interacting with one or more agents in an interaction. The computer implemented method also includes identifying a plurality of phases for the interaction. The computer implemented method further includes extracting features characterizing trust dynamics from the trust data for at least one interaction for each participant of the plurality participants. The at least one interaction is between the participant and an agent of the one or more agents. The computer implemented yet further includes assigning the features characterizing trust dynamics to a phase of the plurality of phases. The computer implemented method includes grouping a subset of the participant of the plurality of participants based on the on features characterizing trust dynamics.

Abstract: A system and method for detecting a perceived level of driver discomfort in an automated vehicle that include receiving image data associated with a driving scene of an ego vehicle, dynamic data associated with an operation of the ego vehicle, and driver data associated with a driver of the ego vehicle during autonomous operation of the ego vehicle. The system and method also include analyzing the image data, the dynamic data, and the driver data and extracting features associated with a plurality of modalities. The system and method additionally include analyzing the extracted features and detecting the perceived level of driver discomfort. The system and method further include analyzing the perceived level of driver discomfort and detecting a probable driver takeover intent of the driver of the ego vehicle to takeover manual operation of the ego vehicle.

Abstract: According to one aspect, a system for trust calibration may include a processor and a memory. 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, such as receiving a record of one or more interactions between a user and a first autonomous device, building a trust profile for the user based on one or more of the interactions between the user and the first autonomous device, and operating a target autonomous device based on the trust profile.

Abstract: A system and method for providing an RNN-based human trust model that include receiving a plurality of inputs related to an autonomous operation of a vehicle and a driving scene of the vehicle and analyzing the plurality of inputs to determine automation variables and scene variables. The system and method also include outputting a short-term trust recurrent neural network state that captures an effect of the driver's experience with respect to an instantaneous vehicle maneuver and a long-term trust recurrent neural network state that captures the effect of the driver's experience with respect to the autonomous operation of the vehicle during a traffic scenario. The system and method further include predicting a take-over intent of the driver to take over control of the vehicle from an automated operation of the vehicle during the traffic scenario.

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: An adaptive trust calibration based autonomous vehicle may include vehicle systems, a system behavior controller, and a driving automation controller. The system behavior controller may generate a driving automation signal indicative of a desired autonomous driving adaptation. The driving automation controller may control the vehicle systems based on parameters including a desired velocity, current velocity of the autonomous vehicle, desired minimum gap distance between the autonomous vehicle and a detected object, current gap distance gap between the autonomous vehicle and a detected object, relative velocity of the detected object with respect to the autonomous vehicle, desired time headway, desired maximum acceleration, desired braking deceleration, and an exponent.

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 system and method for improving driver situation awareness prediction using human visual sensory and memory mechanism that includes receiving data associated with a driving scene of a vehicle and an eye gaze of a driver of the vehicle. The system and method also include analyzing the data and extracting features associated with objects located within the driving scene and determining a situational awareness score that is associated with a situational awareness of the driver with respect to each of the objects located within the driving scene. The system and method further include communicating control signals to electronically control at least one system of the vehicle based on the situational awareness score that is associated with each of the objects located within the driving scene.

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.