Gm Cruise Holdings LLC

Daniel Tien

Feng Tian

Geoffrey Louis Chi-Johnston

Laura Athena Freeman

Seunghyun Min

Lei Huang

Christopher Brian Roland

Diego Plascencia-Vega

Ou Jin

The present technology is directed to training and using a machine learning model to predict a likelihood of a counterfactual safety critical event in autonomous vehicle (AV) driving in a projected scenario occurring after a human takes over control of an AV. An AV management system can identify driving data collected from periods around an occurrence of a human take over event where a human takes over control of an AV. The AV management system can project a scenario that would have resulted if the human did not take over control of the AV based on the driving data and output a counterfactual safety score for the projected scenario. The counterfactual safety score can indicate a probability of a counterfactual collision between the AV and the at least one object in the projected scenario.

Safety measurement for a counterfactual risk in autonomous vehicle driving

System, methods, and computer-readable media for configuring an autonomous vehicle based on safety scores determined by a safety score prediction algorithm, and an associated training technique, to output a safety score for a predicted and/or actual path. The safety score is a value indicating a likelihood of risky events per distance. The safety score prediction algorithm is trained with historical human driving datasets associated with paths (predicted or actual) taken by one or more AVs during human driving.

Baselining autonomous vehicle safety using driving data of human autonomous vehicle operators

The present technology is directed to training and the use of a machine learning model to measure the safety of an autonomous vehicle (AV) driving in simulation. An AV management system can run a simulation of an AV autonomously piloting itself and collect simulation driving data. Further, the AV management system can parse the simulation driving data into kinematic and semantic environmental features and output a simulation safety score of the simulation based on the kinematic and semantic environmental features. The simulation safety score indicates a probability of a safety critical event such as a collision or a near-miss between the AV and the at least one simulated object in the simulation.

Safety measurement of autonomous vehicle driving in simulation

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San Francisco, CA, United States of America

Daniel Tien