Other

Huawei Technologies Co., Limited

Huawei Technologies Canada Co., Ltd.

Kasra Rezaee

Peyman Yadmellat

Daniel Mark Graves

Barry J Gilhuly

Stephen L Smith

Armin Sadeghi

A method or system for adaptive vehicle spacing, including determining a current state of a vehicle based on sensor data captured by sensors of the vehicle; for each possible action in a set of possible actions: (i) predicting based on the current vehicle state a future state for the vehicle, and (ii) predicting, based on the current vehicle state a first zone future safety value corresponding to a first safety zone of the vehicle; and selecting, based on the predicted future states and first zone future safety values for each of the possible actions in the set, a vehicle action.

Method and system for adaptively controlling object spacing

Methods and systems for training a motion planner for an autonomous vehicle are described. A trajectory evaluator agent of the motion planner receives state data defining a current state of the autonomous vehicle and an environment at a current time step. Based on the current state, a trajectory is selected. A reward is calculated based on performance of the selected trajectory in the current state. State data is received for a next state of the autonomous vehicle and the environment at a next time step. Parameters of the trajectory evaluator agent are updated based on the current state, selected trajectory, computed reward and next state. The parameters of the trajectory evaluator agent are updated to assign an evaluation value for the selected trajectory that reflects the calculated reward and expected performance of the selected trajectory in the future states.

Predictive motion planning system and method

Systems, methods and computer-readable media for selecting a trajectory for an autonomous vehicle are disclosed that include computing a current vehicle state for the autonomous vehicle based on observations by a sensing system; computing respective collision probability scores for a plurality of candidate trajectories based on the current vehicle state; computing respective information gain scores for the plurality of candidate trajectories based on the current vehicle state, the information gain score for each candidate trajectory indicating an respective information gain for a next planning horizon interval that is subsequent to the current planning horizon interval; and selecting a planned trajectory from the plurality of candidate trajectories based on the respective collision probability scores and respective information gain scores.

Systems, methods, and media for occlusion-aware motion planning

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Kitchener, Canada

Kasra Rezaee