Honda Motor Co., Ltd.

Sangjae Bae

David Francis Isele

Kikuo Fujimura

Chiho Choi

Alireza Nakhaei Sarvedani

Manish Saroya

Alexandre Miranda Anon

Dhruv Mauria Saxena

Srikanth Malla

Ehsan Moradi Pari

Faizan M Tariq

Ahmadreza Moradipari

Siyu Dai

A method for generating operable driving areas for an autonomous driving vehicle based on a path trajectory of the autonomous driving vehicle is provided. The method may form a space time (ST) graph indicating a distance of travel along the path trajectory with respect to time of the autonomous driving vehicle and path trajectories of devices intersecting with the path trajectory of the autonomous driving vehicle. The method may segment the ST graph into cells, wherein viable cells represent discretized viable unoccupied spaces in the ST graph. The method may find passage ways for the autonomous driving vehicle based on the viable cells. The method may select a desired passage way using quadratic programming (QP) optimization when multiple passage ways are found.

Multi-profile quadratic programming (MPQP) for optimal gap selection and speed planning of autonomous driving

A method for generating operable driving areas for an autonomous driving vehicle based on a path trajectory of the autonomous driving vehicle is provided. The method may form a space time (ST) graph indicating a distance of travel along the path trajectory with respect to time of the autonomous driving vehicle and path trajectories of devices intersecting with the path trajectory of the autonomous driving vehicle, wherein the path trajectory of each device is based on current and historical data for each device. The method may segment the ST graph into cells, wherein viable cells represent discretized viable unoccupied spaces in the ST graph. The method may find passage ways for the autonomous driving vehicle based on the viable cells. The method may select a desired passage way using quadratic programming (QP) optimization when multiple passage ways are found.

Systems and methods for parameter prediction for agent modeling are provided. In one embodiment, a method includes determining a first driving code for a first proximate agent at a first time. The method also includes determining a first set of agent specific parameters for the first proximate agent based on the first driving code. The method further includes calculating a first predicted acceleration for the first proximate agent based on the first set of agent specific parameters. The first predicted acceleration is for a second time after the first time. The method yet further includes determining a first steering parameter for the first proximate agent. The method includes determining a first predicted behavior of the first proximate agent based on the first predicted acceleration and the first steering parameter. The method also includes controlling a host agent based on the first predicted behavior of the first proximate agent.

Systems and methods for parameter prediction for agent modeling

Aspects related to game theoretic decision making may be implemented utilizing a sensor, a memory, and a processor. The sensor may detect other vehicles and corresponding attributes as an observation. The memory may store instructions. The processor may execute the instructions to perform acts, actions, or steps, such as constructing a search tree based on the observation, an initial belief, and a vehicle identified as a current opponent vehicle, performing a Monte Carlo Tree Search (MCTS) on the search tree based on a planning horizon and a time allowance to determine a desired action from a set of ego-actions, executing, via vehicle systems, the desired action, detecting an updated observation associated with one or more of the other vehicles, identifying the other vehicles to be updated as the current opponent vehicle, and updating a root node of the search tree based on the current opponent vehicle.

Game theoretic decision making

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.

System and method for completing trajectory prediction from agent-augmented environments

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.

System and methods for speed and lane advisory in evaluating lane change events and opportunities

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.

Lane selection

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.

System and method for providing cooperation-aware lane change control in dense traffic

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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Berkley, CA, United States of America

Sangjae Bae