Abstract: The disclosure includes embodiments of personalizing a shared ride in a mobility-on-demand service using a response matrix. A method includes receiving feedback from a first set of users that share a first shared ride, wherein the feedback describes their individual satisfaction with the first shared ride. The method includes updating, by the processor, a response matrix to include the feedback, wherein the response matrix includes digital data describing historical user satisfaction with a plurality of shared rides over time. The method includes matching, by the processor, a second set of users to a second shared ride based on service profile data for the users, vehicle data for vehicles, and the response matrix so that the satisfaction of the second set of users with the second shared ride is improved based on the response matrix.

Abstract: A method for managing moving agents is provided. The method comprises identifying goods agents, moving agents, and a plurality of requests within a predetermined area, generating a nodal graph including the moving agents, requests, and goods as vertices and edges defining relations between two vertices, obtaining actions for the moving agents by inputting the nodal graph to a reinforcement-learning based graphical neural network model stored in the moving agents, the reinforcement-learning based graphical neural network outputs the action for the moving agent in response to receiving the nodal graph, and instructing the moving agents to operate based on the actions to satisfy at least one of the plurality of requests.

Abstract: A learning-based lane change prediction algorithm, and systems and methods for implementing the algorithm, are disclosed. The prediction algorithm evaluates the driving behaviors of a target human driver and predicts lane change maneuvers based on those personalized driving behaviors. The algorithm may include an online lane change decision prediction phase and an offline prediction training and cost function recovery phase. During the offline training phase, a machine learning model may be trained based on historical vehicle states. During the online validation phase, driving data may be collected and fed to the trained model to predict a driver's lane change maneuver, identify potential vehicle trajectories, and determine a most probable vehicle trajectory based on a driver's cost function recovered during the offline phase.

Abstract: A learning-based lane change prediction algorithm, and systems and methods for implementing the algorithm, are disclosed. The prediction algorithm evaluates the driving behaviors of a target human driver and predicts lane change maneuvers based on those personalized driving behaviors. The algorithm may include an online lane change decision prediction phase and an offline prediction training and cost function recovery phase. During the offline training phase, a machine learning model may be trained based on historical vehicle states. During the online validation phase, driving data may be collected and fed to the trained model to predict a driver's lane change maneuver, identify potential vehicle trajectories, and determine a most probable vehicle trajectory based on a driver's cost function recovered during the offline phase.

Abstract: A health risk assessment system for estimating a health risk posed by transporting a user in a vehicle. The system includes one or more processors and a memory communicably coupled to the one or more processors and storing a health risk assessment module. The health risk assessment module includes computer-readable instructions that when executed by the one or more processors cause the one or more processors to attempt to acquire information regarding a travel history of the user. The module is also configured to, using acquired travel history information, determine a value of a first health status parameter and to compare the value of the first health status parameter to a first threshold. Responsive to the value of the first health status parameter exceeding the threshold, the module controls operation of the vehicle to implement at least one infection prevention measure.

Abstract: In some implementations, a method for providing a decentralized parking fulfillment service may include executing, by a first processor of a connected computing device, a first artificial intelligence network. In addition, the decentralized parking fulfillment service may include executing, by a second processor of a connected vehicle, a second artificial intelligence network. The decentralized parking fulfillment service may include providing the decentralized parking fulfillment service including solving, by the first artificial intelligence network and the second artificial intelligence network, a function that determines a parking space for the connected vehicle. In some implementations, a decentralized parking fulfillment system for providing the decentralized parking fulfillment system includes a parking lot agent stored in a first non-transitory memory of the connected computing device and a vehicle parking agent stored in a second non-transitory memory of the connected vehicle.

Abstract: A method includes receiving requests to form a vehicle platoon from a plurality of vehicles, determining terms of a contract for the plurality of vehicles to form the vehicle platoon comprising a lead vehicle and one or more follower vehicles, and transmitting terms of the contract to the plurality of vehicles. The terms of the contract comprise the lead vehicle receiving a first payment, at least one of the follower vehicle making a second payment, and each of the follower vehicles receiving vehicle data from the lead vehicle according to a unique service profile associated with each follower vehicle.

Abstract: Systems and methods for maintaining a service session in an automotive edge computing environment are disclosed herein. One embodiment computes a first estimation result pertaining to computation resources of a current edge server with which a vehicle is in communication; computes a second estimation result pertaining to link quality; computes a third estimation result pertaining to service quality; compares the first, second, and third estimation results with first, second, and third predetermined thresholds, respectively; selects a new edge server with which to maintain the service session and initiates a service migration request, when the first predetermined threshold is triggered; initiates a wireless access handover request, when only the second predetermined threshold is triggered; and selects a new edge server with which to maintain the service session and initiates a service migration request, when only the third predetermined threshold is triggered and one of two predetermined conditions is met.

Abstract: Systems and methods for scheduling environment perception-based data offloading for numerous connected vehicles are disclosed. In one embodiment, a method for offloading data includes capturing an image of a view of interest from a vehicle, segmenting the image into a plurality of blocks, and determining a scheduling priority for each of one or more blocks among the plurality of blocks based on block values, wherein the block values relate to one or more objects of interest contained in each of the one or more blocks. The method further includes offloading, from the vehicle to a server, one or more blocks based on the scheduling priority of the one or more blocks.

Abstract: In accordance with one embodiment of the present disclosure, a system for generating surrounding views includes a controller. The controller may be programmed to perform operations including receiving a status information from a remote source, receiving a surround view system (SVS) request having a set of requirements from a vehicle, identifying one or more remote sources based on the status information of the one or more remote sources and the set of requirements of the SVS request, and directing the one or more remote sources to stream SVS data to the vehicle.

Abstract: System, methods, and embodiments described herein relate to dynamically generating a wide field-of-view three-dimensional pseudo point cloud of an environment around a vehicle. A disclosed method may include capturing, via a camera, a first view in a first image, determining a first depth map based on the first image, obtaining, from an external system, a second image of a second view that overlaps the first view and a second depth map based on the second image, inputting the first image and the second image into a self-supervised homograph network that is trained to output a homographic transformation matrix between the first image and the second image, and generating a three-dimensional pseudo point cloud that combines the first depth map and the second depth map based on the homographic transformation matrix.

Abstract: A method comprises determining a vectorized representation of positions of road agents and road geometry based on sensor data from a vehicle, inputting the vectorized representation of the positions of the road agents and the road geometry into a trained transformer network, predicting one or more road agent trajectories at one or more future time steps based on an output of the transformer network, predicting an acceleration of the vehicle at the one or more future time steps based on the predicted one or more road agent trajectories at the one or more future time steps, and causing the vehicle to perform the predicted acceleration at the one or more future time steps.

Abstract: Systems, methods, and computer program products that are configured to identify or otherwise detect the presence of bacteria, classify the identified or detected bacteria, and also predict the growth of the classified bacteria on various touchable surfaces within a vehicle passenger cabin or compartment. Such systems, methods, and computer program products are configured to identify/detect, classify, and predict the presence and/or growth of bacteria, and transmit one or more alerts, warnings, and/or reports to vehicle owners, service providers, and/or occupants based on the identification/detection, classification, and prediction.

Abstract: Systems and methods are provided for predictive assessment of driver perception abilities based on driving behavior personalized to the driver in connection with, but not necessarily, autonomous and semi-autonomous vehicles. In accordance with on embodiment, a method comprises receiving first vehicle operating data and associated first gaze data of a driver operating a vehicle; training a model for the driver based on the first vehicle operating data and the first gaze data, the model indicating driving behavior of the driver; receiving second vehicle operating data and associated second gaze data of the driver; and determining that an ability of the driver to perceive hazards is impaired based on applying the model to the second vehicle operating data and associated second gaze data.

Abstract: A rest stop recommendation system monitors driving behavior of a driver and stores information indicating the driving behavior as historical data, determines, based at least in part on the historical data, a driver tiredness state, a continuous driving length preference, and a refueling pattern preference, determines a time to recommend a rest stop based at least in part on the driver tiredness state, the continuous driving length preference, and the refueling pattern preference, extracts, from a map database, a plurality of rest stops within a predetermined radius of a position of the vehicle, determines rest stop characteristic preferences based at least in part on the historical data, selects one or more potential rest stops from among the plurality of rest stops based at least in part on the rest stop characteristic preferences, and presents the one or more potential rest stops to the driver in a recommendation.

Abstract: A vehicle includes a controller programed to: collect a set of data related to a driver of the vehicle; predict a driving setting for the driver using the set of data and an initial student-T process (STP) machine learning (ML) model; generate an updated STP ML model based on the prediction of the driving setting as to the set of vehicle data; transmit incremental learning related to the updated STP ML model to a server; and receive, from the server, a personalized driving setting for the driver output from a cloud STP ML model trained by the incremental learning.

Abstract: Systems and methods are provided for utilizing sensor data from sensors of different modalities and from different vehicles to generate a combined image of an environment. Sensor data, such as a point cloud, generated by a LiDAR sensor on a first vehicle may be combined with sensor data, such as image data, generated by a camera on a second vehicle. The point cloud and image data may be combined to provide benefits over either data individually and processed to provide an improved image of the environment of the first and second vehicles. Either vehicle can perform this processing when receiving the sensor data from the other vehicle. An external system can also do the processing when receiving the sensor data from both vehicles. The improved image can then be used by one or both of the vehicles to improve, for example, automated travel through or obstacle identification in the environment.

Abstract: Described are systems and methods for substituting missing graph information caused during the transmission of graph information from one entity to another. In one example, a system includes a processor and a memory with machine-readable instructions that cause the processor to receive an incoming graph stream comprising a plurality of graphs having detected object information and at least one missing graph, transform the plurality of graphs into incoming embedded vectors having values that represent detected object information of the incoming graph stream, provide the embedded vector that corresponds to the at least one missing graph with a missing graph value, and substitute the missing graph value with a substitute value using a recurrent neural network that interpolates the substitute value using the incoming embedded vectors that are not equal to the missing graph value.

Abstract: Connected vehicles and methods described herein provide for message sender identification. Connected vehicles and methods disclosed herein can include generating a message based hyper-graph based on the positions of connected elements of a road traffic network as communicated to the connected vehicle by the connected elements. Connected vehicles and methods disclosed herein can include generating, a perception based hyper-graph based on the perceived positions of at least a subset of the elements of the road traffic network as determined by a sensor of the vehicle. Connected vehicles and methods disclosed herein can include matching a node of the message-based hyper-graph to a corresponding node of the perception-based hyper-graph to determine the sender of a received message.

Abstract: A method for data sharing between a transmitter vehicle and a receiver vehicle includes obtaining sensor data, encoding the sensor data into structured data having a set of features and a set of channels, generating a channel attention map based on inter-channel relationships of the set of features, generating weights corresponding to channels of the channel attention map, selecting one or more channels among the set of channels based on the weights corresponding to the set of channels, and transmitting the selected channels to the receiver vehicle.