Abstract: According to one aspect, a host vehicle (HV) equipped with a system for target classification may include a communication unit receiving a first message from a remote vehicle (RV) at a first time. The communication unit may receive a second message from the RV at a second time. An operation unit may append the path history position trail information of the first message with the path history position trail information of the second message based on an overlap between the path history position trail information of the first message and the second message to determine an overall path history position trail for the RV and calculate a lane level position offset between the HV and the RV based on the overall path history position trail for the RV and a current position of the HV and a result of lane change detection module of the HV about the HV and RV lane changes.

Abstract: A method for traffic state estimation of a road network based on a plurality of vehicles including probe vehicles and non-probe vehicles travelling includes receiving probe vehicle data from the probe vehicles within a communication range of a host vehicle. The method also includes spatially and temporally associating the probe vehicle data to lane level cells of the road network, and identifying empty lane level cells of the road network where the probe vehicle data is unavailable. The method includes calculating estimated non-probe vehicle data for the empty lane level cells based on the probe vehicle data. The method further includes calculating a traffic density value for the road network based on the probe vehicle data and the estimated non-probe vehicle data and providing the traffic density value to the host vehicle.

Abstract: A system and method for controlling a merge vehicle travelling along a highway having a merge lane and a main lane. The method includes receiving merge data about the merge vehicle and a surrounding environment of the merge vehicle. The method includes detecting an intent to perform a merge maneuver by the merge vehicle from the merge lane to the main lane based on the merge data. Further, the method includes generating a graph having a plurality of nodes connected by edges. The plurality of nodes includes a start node set to a current position of the merge vehicle and a goal node located in the main lane after the merge lane has ended. The method includes calculating a three-dimensional (3D) trajectory based on the graph by optimizing edge costs from the start node to the goal node, and controlling the merge vehicle based on the 3D trajectory.

Abstract: Systems and methods for detecting a traffic event include receiving vehicle data from a plurality of vehicles travelling along a roadway. The method also includes where the roadway is discretized at a cell level into a plurality of cells. The method also includes identifying vehicles of interest that are within a communication range of a host vehicle. Each of the vehicles of interest are associated with a cell of interest from the plurality of cells. Further, the method includes for each cell of interest at each timestep according to a measurement update interval, calculating an average deceleration based on vehicle data transmitted from each of the vehicles of interest associated with the cell of interest. Additionally, the method includes detecting the traffic event based on the average deceleration for each cell of interest at each timestep.

Abstract: Systems and methods for cooperative ramp merger are described. In one embodiment, a system includes a swarm module, position module, identification module, constraint module, and cooperative module. The swarm module causes a host vehicle to join cooperating vehicles of proximate vehicles to assist the merging vehicle. A position module identifies relative positions of the host vehicle and the proximate vehicles. An identification module selects a role for the host vehicle based on the relative positions of the host vehicle and the cooperating vehicles and identifies a duplicate role. A constraint module calculates a merge location area based on the relative positions of the host vehicle and the proximate vehicles and the ramp location and a duplicate sequence based on the duplicate role. A cooperative module determines a cooperative action for the host vehicle based on the role of the host vehicle, the merge location area, and the duplicate role.

Abstract: According to one aspect, a host vehicle (HV) equipped with a system for target classification may include a communication unit receiving a first message from a remote vehicle (RV) at a first time. The communication unit may receive a second message from the RV at a second time. An operation unit may append the path history position trail information of the first message with the path history position trail information of the second message based on an overlap between the path history position trail information of the first message and the second message to determine an overall path history position trail for the RV and calculate a lane level position offset between the HV and the RV based on the overall path history position trail for the RV and a current position of the HV and a result of lane change detection module of the HV about the HV and RV lane changes.

Abstract: A vehicle includes a frame having a left side and a right side, a plurality of wheels including a plurality of left wheels at the left side of the frame and a plurality of right wheels at the right side of the frame, and a vehicle stability system including an actuator device selectively actuated to balance the vehicle on either the plurality of left wheels or the plurality of right wheels while maintaining a space between the other of the plurality of left wheels or the plurality of right wheels and a ground surface.

Abstract: A method for traffic state estimation of a road network based on a plurality of vehicles including probe vehicles and non-probe vehicles travelling includes receiving probe vehicle data from the probe vehicles within a communication range of a host vehicle. The method also includes spatially and temporally associating the probe vehicle data to lane level cells of the road network, and identifying empty lane level cells of the road network where the probe vehicle data is unavailable. The method includes calculating estimated non-probe vehicle data for the empty lane level cells based on the probe vehicle data. The method further includes calculating a traffic density value for the road network based on the probe vehicle data and the estimated non-probe vehicle data and providing the traffic density value to the host vehicle.

Abstract: Systems and methods for cooperative ramp merger are described herein. In one embodiment, a system for assisting with a merge based on a ramp location of a ramp is provided. A swarm module causes a host vehicle to join a swarm created to assist the merging vehicle in the merge maneuver. A position module identifies relative positions of the host vehicle and other members of the swarm. An identification module selects a role for the host vehicle based on the relative positions of the host vehicle and the other members of the plurality of members. A constraint module calculates a merge location area based on the relative positions of the host vehicle and the other members of the plurality of members and the ramp location. A cooperative module determines a cooperative action for the host vehicle based on the role of the host vehicle and the merge location area.

Abstract: Systems and methods for cooperative ramp merger are described herein. In one embodiment, a system for assisting with a merge based on a ramp location of a ramp is provided. A swarm module causes a host vehicle to join a swarm created to assist the merging vehicle in the merge maneuver. A position module identifies relative positions of the host vehicle and other members of the swarm. An identification module selects a role for the host vehicle based on the relative positions of the host vehicle and the other members of the plurality of members. A constraint module calculates a merge location area based on the relative positions of the host vehicle and the other members of the plurality of members and the ramp location. A cooperative module determines a cooperative action for the host vehicle based on the role of the host vehicle and the merge location area.

Abstract: Systems and methods for cooperative ramp merger are described. In one embodiment, a system includes a swarm module, position module, identification module, constraint module, and cooperative module. The swarm module causes a host vehicle to join cooperating vehicles of proximate vehicles to assist the merging vehicle. A position module identifies relative positions of the host vehicle and the proximate vehicles. An identification module selects a role for the host vehicle based on the relative positions of the host vehicle and the cooperating vehicles and identifies a duplicate role. A constraint module calculates a merge location area based on the relative positions of the host vehicle and the proximate vehicles and the ramp location and a duplicate sequence based on the duplicate role. A cooperative module determines a cooperative action for the host vehicle based on the role of the host vehicle, the merge location area, and the duplicate role.

Abstract: Systems and methods for a cooperative autonomy framework are described. According to one embodiment, a cooperative autonomy framework includes a goal module, a target module, a negotiation module, and a perception module. The goal module determines a cooperation goal. The target module identifies a vehicle associated with the cooperation goal and sends a swarm request to the vehicle to join a swarm. The negotiation module receives a swarm acceptance from the vehicle. The perception module determines a cooperative action for the vehicle relative to the swarm.