Nvidia Corporation

Hon Leung Lee

David Nister

Julia C Ng

Yizhou Wang

Birgit Henke

Rotem Aviv

Raul Correal Tezanos

Yunfei Shi

Nikolai Smolyanskiy

Alexey Kamenev

Amir Akbarzadeh

Joshua John Bialkowski

Miguel Sainz Serra

Zhenyi Zhang

Ollin Boer Bohan

Hans Jonas Nilsson

Anton Vorontsov

Seungho Lee

Fangkai Yang

Sachin Pullaikudi Veedu

Charles Jerome Vorbach, Jr

Hanne Buur

Hermes Lanker

Embodiments of the present disclosure relate to a method of translating routes between maps. The method may include obtaining a graph based on data of an area. The graph may include one or more nodes representing different locations along one or more navigable paths as defined by the map. The method may also include obtaining one or more waypoints that define a route to traverse in the area and selecting, from the nodes, one or more path nodes based on locations of the path nodes corresponding to locations of the way points. The selected path nodes may define a path in the data that corresponds to the route.

Translating route information between data structures for autonomous systems and applications

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

Embodiments of the present disclosure relate to behavior planning for autonomous vehicles. The technology described herein selects a preferred trajectory for an autonomous vehicle based on an evaluation of multiple hypothetical trajectories by different components within a planning system. The various components provide an optimization score for each trajectory according to the priorities of the component and scores from multiple components may form a final optimization score. This scoring system allows the competing priorities (e.g., comfort, minimal travel time, fuel economy) of different components to be considered together. In examples, the trajectory with the best combined score may be selected for implementation. As such, an iterative approach that evaluates various factors may be used to identify an optimal or preferred trajectory for an autonomous vehicle when navigating an environment.

Behavior planning for autonomous vehicles

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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Bellevue, WA, United States of America

Hon Leung Lee