Waymo LLC

Haoyu Chen

Wei Chai

Khaled Refaat

Kai Ding

Minfa Wang

Stephane Ross

Maher Mneimneh

Johan Engstrom

Menghui Wang

Alisha Saxena

Isaac Supeene

Azadeh Dinparastdjadid

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing knowledge distillation for autonomous vehicles. One of the methods includes obtaining sensor data characterizing an environment, wherein the sensor data has been captured by one or more sensors on-board a vehicle in the environment; processing, for each of one or more surrounding agents in the environment, a network input generated from the sensor data using a neural network to generate an agent discomfort prediction that characterizes a level of discomfort of the agent; combining the one or more agent discomfort predictions to generate an aggregated discomfort score; and providing the aggregated discomfort score to a path planning system of the vehicle in order to generate a future path of the vehicle.

Knowledge distillation for autonomous vehicles

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for computing a backward looking surprise metric for autonomously driven vehicles. One of the methods includes obtaining first data representing one or more previously predicted states of an agent along one or more predicted trajectories of the agent at a first time step. Second data representing one or more states of the agent at a subsequent time step is obtained. A surprise score is computed from a measure of a difference between the first data computed for the one or more predicted trajectories for the prior time step and the second data computed for the one or more predicted states for the subsequent time step.

Assessing surprise for autonomous vehicles

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for identifying high-priority agents in the vicinity of a vehicle. The high-priority agents can be identified based on a set of mutual importance scores in which each mutual importance score indicates an estimated mutual relevance between the vehicle and a different agent from a set of agents on planning decisions of the other. The mutual importance scores can be calculated based on importance scores assessed from the perspectives of both the vehicle and the agents.

Ranking agents near autonomous vehicles by mutual importance

Data representing a set of predicted trajectories and a planned trajectory for an autonomous vehicle is obtained. A predictability score for the planned trajectory can be determined based on a comparison of the planned trajectory to the set of predicted trajectories for the autonomous vehicle. The predictability score indicates a level of predictability of the planned trajectory. A determination can be made, based at least on the predictability score, whether to initiate travel with the autonomous vehicle along the planned trajectory. In response to determining to initiate travel with the autonomous vehicle along the planned trajectory, a control system can be directed to maneuver the autonomous vehicle along the planned trajectory.

Predictability-based autonomous vehicle trajectory assessments

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Sunnyvale, CA, United States of America

Haoyu Chen