Abstract: A cooperative driving system includes a processor and a memory having one or more modules. The modules cause the processor to determine a route that includes a road segment to a destination for an ego vehicle based on (1) an ego vehicle operating parameter that has information regarding a preferred operation of an ego vehicle by an occupant, and (2) a location of a like vehicle with respect to the ego vehicle when the ego vehicle is traveling on at least a portion of the route. The one or more modules also cause the processor to determine a road-segment-level decision for the ego vehicle for the road segment based on the ego vehicle operating parameter, communicate with the like vehicle near the road segment to negotiate the road-segment-level decision to generate a negotiated road-segment-level decision, and maneuver the ego vehicle inside the road segment using the negotiated road-segment-level decision.

Abstract: The disclosure includes embodiments for message management by an ego vehicle for a Vehicle-to-Everything (V2X) channel used in V2X messages for providing a cooperative driving service. In some embodiments, the method includes receiving a first V2X message including remote sensor data recorded by a remote vehicle. The method includes causing an onboard sensor set of the ego vehicle to record ego sensor data. The method includes determining a channel busy ratio of the V2X channel. The method includes determining a set of factors affecting a state of a recipient vehicle that receives a second V2X message. The method includes selecting a cooperation message species class and an aggregation message species class of the second V2X message based on the set of factors and the state of the recipient vehicle. The method includes selecting, from a set of available classes, a specific class of the second V2X message.

Abstract: Systems and methods for distributed cooperative localization in a connected vehicular platform are disclosed herein. At a first sensor-rich vehicle, one embodiment receives, from a second sensor-rich vehicle via direct vehicle-to-vehicle (V2V) communication, an estimated location of the first sensor-rich vehicle and an associated confidence level; estimates a location of the second sensor-rich vehicle based on first sensor data and assigns a confidence level to the estimated location; transmits to the second sensor-rich vehicle via direct V2V communication, the estimated location of the second sensor-rich vehicle and the assigned confidence level; accepts, based on communication between the first and second sensor-rich vehicles and predetermined acceptance criteria, an assignment to perform localization for a legacy vehicle; estimates a location of the legacy vehicle based on second sensor data; and transmits, to the legacy vehicle, the estimated location of the legacy vehicle.

Abstract: System, methods, and other embodiments described herein relate to improving monitoring of traffic flows. In one embodiment, a method includes aggregating perception data associated with a road network from information sources to a server over a network. The method also includes generating a graph structure from the perception data in association with a neural network model. The graph structure is an incomplete representation of the road network in view of missing data. The method also includes completing the graph structure using the neural network model that forms a graph model of the traffic flows to de-noise the graph structure according to road constraints between two points in the road network. The method also includes communicating the graph model of the traffic flows to a vehicle to navigate traffic in the road network.

Abstract: A cooperative driving system includes a processor and a memory having one or more modules. The modules cause the processor to determine a route that includes a road segment to a destination for an ego vehicle based on (1) an ego vehicle operating parameter that has information regarding a preferred operation of an ego vehicle by an occupant, and (2) a location of a like vehicle with respect to the ego vehicle when the ego vehicle is traveling on at least a portion of the route. The one or more modules also cause the processor to determine a road-segment-level decision for the ego vehicle for the road segment based on the ego vehicle operating parameter, communicate with the like vehicle near the road segment to negotiate the road-segment-level decision to generate a negotiated road-segment-level decision, and maneuver the ego vehicle inside the road segment using the negotiated road-segment-level decision.

Abstract: The disclosure includes embodiments for message management by an ego vehicle for a Vehicle-to-Everything (V2X) channel used in V2X messages for providing a cooperative driving service. In some embodiments, the method includes receiving a first V2X message including remote sensor data recorded by a remote vehicle. The method includes causing an onboard sensor set of the ego vehicle to record ego sensor data. The method includes determining a channel busy ratio of the V2X channel. The method includes determining a set of factors affecting a state of a recipient vehicle that receives a second V2X message. The method includes selecting a cooperation message species class and an aggregation message species class of the second V2X message based on the set of factors and the state of the recipient vehicle. The method includes selecting, from a set of available classes, a specific class of the second V2X message.

Abstract: An edge device can send a vehicle-specific alert to a connected vehicle, which is configured to communicate with the edge device. The edge device can receive observations of other vehicles on the road from connected vehicles and/or roadside units. The edge device can also receive driving history and/or other information about the other vehicles from a server. The edge device can classify the behavior of vehicles detected in the observations based on the observations, driving history, and/or other information. Based on the classified behavior, the edge device can determine whether and how the connected vehicle is impacted and, if impacted, send the vehicle-specific alert to the connected vehicle.

Abstract: System, methods, and other embodiments described herein relate to improving monitoring of traffic flows. In one embodiment, a method includes aggregating perception data associated with a road network from information sources to a server over a network. The method also includes generating a graph structure from the perception data in association with a neural network model. The graph structure is an incomplete representation of the road network in view of missing data. The method also includes completing the graph structure using the neural network model that forms a graph model of the traffic flows to de-noise the graph structure according to road constraints between two points in the road network. The method also includes communicating the graph model of the traffic flows to a vehicle to navigate traffic in the road network.

Abstract: A method of generating a traffic prediction includes receiving traffic data from a plurality of reporting sources, forming a plurality of initial graphs, generating a plurality of completed graphs based on the plurality of initial graphs by at least removing noise from the plurality of initial graphs, generating a plurality of feature vectors that represent spatial relationships and temporal relationships in the plurality of completed graphs, outputting a first feature vector corresponding to a first time window as a first output, caching a copy of the first feature vector with a set of feature vectors of the plurality of feature vectors corresponding to a second time window, connecting the first feature vector with the set of feature vectors, outputting the result as a second output, fusing the first output with the second output and generating a traffic prediction based on the fused outputs.

Abstract: Systems and methods for distributed cooperative localization in a connected vehicular platform are disclosed herein. At a first sensor-rich vehicle, one embodiment receives, from a second sensor-rich vehicle via direct vehicle-to-vehicle (V2V) communication, an estimated location of the first sensor-rich vehicle and an associated confidence level; estimates a location of the second sensor-rich vehicle based on first sensor data and assigns a confidence level to the estimated location; transmits to the second sensor-rich vehicle via direct V2V communication, the estimated location of the second sensor-rich vehicle and the assigned confidence level; accepts, based on communication between the first and second sensor-rich vehicles and predetermined acceptance criteria, an assignment to perform localization for a legacy vehicle; estimates a location of the legacy vehicle based on second sensor data; and transmits, to the legacy vehicle, the estimated location of the legacy vehicle.

Abstract: An edge device can send a vehicle-specific alert to a connected vehicle, which is configured to communicate with the edge device. The edge device can receive observations of other vehicles on the road from connected vehicles and/or roadside units. The edge device can also receive driving history and/or other information about the other vehicles from a server. The edge device can classify the behavior of vehicles detected in the observations based on the observations, driving history, and/or other information. Based on the classified behavior, the edge device can determine whether and how the connected vehicle is impacted and, if impacted, send the vehicle-specific alert to the connected vehicle.