Nvidia Corporation

Yunfei Shi

David Nister

Julia C Ng

Hon Leung Lee

Yizhou Wang

Birgit Henke

Rotem Aviv

Hans Jonas Nilsson

Fangkai Yang

Sachin Pullaikudi Veedu

Charles Jerome Vorbach, Jr

Hanne Buur

In various examples, a safety decomposition architecture for autonomous machine applications is presented that uses two or more individual safety assessments to satisfy a higher safety integrity level (e.g., ASIL D). For example, a behavior planner may be used as a primary planning component, and a collision avoidance feature may be used as a diverse safety monitoring component—such that both may redundantly and independently prevent violation of safety goals. In addition, robustness of the system may be improved as single point and systematic failures may be avoided due to the requirement that two independent failures—e.g., of the behavior planner component and the collision avoidance component—occur simultaneously to cause a violation of the safety goals.

Safety decomposition architecture for autonomous machine applications

In various examples, a yield scenario may be identified for a first vehicle. A wait element is received that encodes a first path for the first vehicle to traverse a yield area and a second path for a second vehicle to traverse the yield area. The first path is employed to determine a first trajectory in the yield area for the first vehicle based at least on a first location of the first vehicle at a time and the second path is employed to determine a second trajectory in the yield area for the second vehicle based at least on a second location of the second vehicle at the time. To operate the first vehicle in accordance with a wait state, it may be determined whether there is a conflict between the first trajectory and the second trajectory, where the wait state defines a yielding behavior for the first vehicle.

Behavior planning for autonomous vehicles in yield scenarios

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Santa Clara, CA, United States of America

Yunfei Shi