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公开(公告)号：US11608083B2
公开(公告)日：2023-03-21
申请号：US16844331
申请日：2020-04-09
摘要： A system and method for providing cooperation-aware lane change control in dense traffic that include receiving vehicle dynamic data associated with an ego vehicle and receiving environment data associated with a surrounding environment of the ego vehicle. The system and method also include utilizing a controller that includes an analyzer to analyze the vehicle dynamic data and a recurrent neural network to analyze the environment data. The system and method further include executing a heuristic algorithm that sequentially evaluates the future states of the ego vehicle and the predicted interactive motions of the surrounding vehicles to promote the cooperation-aware lane change control in the dense traffic.
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公开(公告)号：US12110041B2
公开(公告)日：2024-10-08
申请号：US17161136
申请日：2021-01-28
发明人： Chiho Choi , Srikanth Malla , Sangjae Bae
CPC分类号： B60W60/0027 , G06V20/56 , B60W2420/403 , B60W2554/20 , B60W2554/404
摘要： A system and method for completing trajectory prediction from agent-augmented environments that include receiving image data associated with surrounding environment of an ego agent and processing an agent-augmented static representation of the surrounding environment of the ego agent based on the image data. The system and method also include processing a set of spatial graphs that correspond to an observation time horizon based on the agent-augmented static representation. The system and method further include predicting future trajectories of agents that are located within the surrounding environment of the ego agent based on the spatial graphs.
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公开(公告)号：US20210078603A1
公开(公告)日：2021-03-18
申请号：US16844331
申请日：2020-04-09
摘要： A system and method for providing cooperation-aware lane change control in dense traffic that include receiving vehicle dynamic data associated with an ego vehicle and receiving environment data associated with a surrounding environment of the ego vehicle. The system and method also include utilizing a controller that includes an analyzer to analyze the vehicle dynamic data and a recurrent neural network to analyze the environment data. The system and method further include executing a heuristic algorithm that sequentially evaluates the future states of the ego vehicle and the predicted interactive motions of the surrounding vehicles to promote the cooperation-aware lane change control in the dense traffic.
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公开(公告)号：US11708092B2
公开(公告)日：2023-07-25
申请号：US17121081
申请日：2020-12-14
发明人： David Francis Isele , Kikuo Fujimura , Sangjae Bae
IPC分类号： B60W60/00 , G08G1/16 , G01C21/34 , B60W30/09 , B60W50/00 , B60W10/20 , B60W30/18 , B60W30/095
CPC分类号： B60W60/0015 , B60W10/20 , B60W30/09 , B60W30/0956 , B60W30/18163 , B60W50/0097 , B60W60/0027 , G01C21/3453 , G08G1/166 , G08G1/167
摘要： According to one aspect, systems and techniques for lane selection may include receiving a current state of an ego vehicle and a traffic participant vehicle, and a goal position, projecting the ego vehicle and the traffic participant vehicle onto a graph network, where nodes of the graph network may be indicative of discretized space within an operating environment, determining a current node for the ego vehicle within the graph network, and determining a subsequent node for the ego vehicle based on identifying adjacent nodes which may be adjacent to the current node, calculating travel times associated with each of the adjacent nodes, calculating step costs associated with each of the adjacent nodes, calculating heuristic costs associated with each of the adjacent nodes, and predicting a position of the traffic participant vehicle.
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公开(公告)号：US20220185326A1
公开(公告)日：2022-06-16
申请号：US17121081
申请日：2020-12-14
发明人： David Francis Isele , Kikuo Fujimura , Sangjae Bae
IPC分类号： B60W60/00 , G08G1/16 , G01C21/34 , B60W30/095 , B60W50/00 , B60W10/20 , B60W30/18 , B60W30/09
摘要： According to one aspect, systems and techniques for lane selection may include receiving a current state of an ego vehicle and a traffic participant vehicle, and a goal position, projecting the ego vehicle and the traffic participant vehicle onto a graph network, where nodes of the graph network may be indicative of discretized space within an operating environment, determining a current node for the ego vehicle within the graph network, and determining a subsequent node for the ego vehicle based on identifying adjacent nodes which may be adjacent to the current node, calculating travel times associated with each of the adjacent nodes, calculating step costs associated with each of the adjacent nodes, calculating heuristic costs associated with each of the adjacent nodes, and predicting a position of the traffic participant vehicle.
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公开(公告)号：US20210086798A1
公开(公告)日：2021-03-25
申请号：US16841602
申请日：2020-04-06
摘要： A system for generating a model-free reinforcement learning policy may include a processor, a memory, and a simulator. The simulator may be implemented via the processor and the memory. The simulator may generate a simulated traffic scenario including two or more lanes, an ego-vehicle, a dead end position, and one or more traffic participants. The dead end position may be a position by which a lane change for the ego-vehicle may be desired. The simulated traffic scenario may be associated with an occupancy map, a relative velocity map, a relative displacement map, and a relative heading map at each time step within the simulated traffic scenario. The simulator may model the ego-vehicle and one or more of the traffic participants using a kinematic bicycle model. The simulator may build a policy based on the simulated traffic scenario using an actor-critic network. The policy may be implemented on an autonomous vehicle.
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7.发明授权公开(公告)号：US12065173B2
公开(公告)日：2024-08-20
申请号：US17707792
申请日：2022-03-29
发明人： Faizan M. Tariq , David F. Isele , Sangjae Bae
IPC分类号： B60W60/00 , B60W40/105
CPC分类号： B60W60/00274 , B60W40/105 , B60W60/00272 , B60W2552/53 , B60W2554/4041 , B60W2554/4042 , B60W2554/4045 , B60W2554/4049
摘要： Systems and methods for speed and lane advisory are provided. In one embodiment, a method includes determining a host longitudinal position in a current lane. The method includes identifying at least one candidate lane adjacent the current lane. The method includes calculating a longitudinal maneuver distance for the host agent to execute a maneuver from the current lane to a candidate. The method includes calculating an augmented minimum distance by combining a predetermined minimum distance and the longitudinal maneuver distance. The method includes calculating a current lane headway distance for the current lane and a first candidate lane headway distance for the first candidate lane. The method includes selecting a target lane from a current lane or a candidate lane based on a comparison of the current lane headway distance and the first candidate lane headway distance and controlling the host agent based on the selection of the target lane.
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公开(公告)号：US11780470B2
公开(公告)日：2023-10-10
申请号：US17229126
申请日：2021-04-13
发明人： Sangjae Bae , David F. Isele , Kikuo Fujimura
CPC分类号： B60W60/0027 , G08G1/0133 , G08G1/0145 , G08G1/167 , B60W2552/10 , B60W2552/53 , B60W2554/4045
摘要： According to one aspect, systems and techniques for lane selection may include receiving a current state of an ego vehicle and a traffic participant vehicle, and a goal position, projecting the ego vehicle and the traffic participant vehicle onto a graph network, where nodes of the graph network may be indicative of discretized space within an operating environment, determining a current node for the ego vehicle within the graph network, and determining a subsequent node for the ego vehicle based on identifying adjacent nodes which may be adjacent to the current node, calculating travel times associated with each of the adjacent nodes, calculating step costs associated with each of the adjacent nodes, calculating heuristic costs associated with each of the adjacent nodes, and predicting a position of the traffic participant vehicle.
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公开(公告)号：US11465650B2
公开(公告)日：2022-10-11
申请号：US16841602
申请日：2020-04-06
摘要： A system for generating a model-free reinforcement learning policy may include a processor, a memory, and a simulator. The simulator may be implemented via the processor and the memory. The simulator may generate a simulated traffic scenario including two or more lanes, an ego-vehicle, a dead end position, and one or more traffic participants. The dead end position may be a position by which a lane change for the ego-vehicle may be desired. The simulated traffic scenario may be associated with an occupancy map, a relative velocity map, a relative displacement map, and a relative heading map at each time step within the simulated traffic scenario. The simulator may model the ego-vehicle and one or more of the traffic participants using a kinematic bicycle model. The simulator may build a policy based on the simulated traffic scenario using an actor-critic network. The policy may be implemented on an autonomous vehicle.
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10.发明申请公开(公告)号：US20220153307A1
公开(公告)日：2022-05-19
申请号：US17161136
申请日：2021-01-28
发明人： Chiho Choi , Srikanth Malla , Sangjae Bae
摘要： A system and method for completing trajectory prediction from agent-augmented environments that include receiving image data associated with surrounding environment of an ego agent and processing an agent-augmented static representation of the surrounding environment of the ego agent based on the image data. The system and method also include processing a set of spatial graphs that correspond to an observation time horizon based on the agent-augmented static representation. The system and method further include predicting future trajectories of agents that are located within the surrounding environment of the ego agent based on the spatial graphs.
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