Nvidia Corporation

Birgit Henke

David Nister

Amir Akbarzadeh

Yu Sheng

Ivana Stojanovic

Michael Kroepfl

Andrew Carley

Enliang Zheng

Julia C Ng

Si Lu

Jugnu Agrawal

Ruchi Bhargava

Yizhou Wang

Rotem Aviv

Hon Leung Lee

Neda Cvijetic

Nikolai Smolyanskiy

Ibrahim Eden

Vaibhav Thukral

Michael Grabner

Alexey Kamenev

Youding Zhu

Joshua John Bialkowski

Miguel Sainz Serra

Zhenyi Zhang

Ollin Boer Bohan

Jeffrey Liu

Vadim Cugunovs

Yunfei Shi

Anton Vorontsov

Seungho Lee

Raul Correal Tezanos

Niharika Arora

Fangkai Yang

Hermes Lanker

Jordan Marr

Embodiments of the present disclosure relate to generating RADAR (RAdio Detection And Ranging) point clouds based on RADAR data obtained from one or more RADAR sensors disposed on one or more ego-machines. In these or other embodiments, the RADAR point clouds may be used to generate map data. Additionally or alternatively, the RADAR point clouds may be used for performing localization.

Sensor data point cloud generation for map creation and localization for autonomous systems and applications

One or more embodiments of the present disclosure relate to generation of map data. In these or other embodiments, the generation of the map data may include determining whether objects indicated by the sensor data are static objects or dynamic objects. Additionally or alternatively, sensor data may be removed or included in the map data based on determinations as to whether it corresponds to static objects or dynamic objects.

Sensor data based map creation for autonomous systems and applications

One or more embodiments of the present disclosure relate to generating RADAR (RAdio Detection And Ranging) point clouds based on RADAR data obtained from one or more RADAR sensors disposed on one or more ego-machines. In these or other embodiments, the RADAR point clouds may be communicated to a distributed map system that is configured to generate map data based on the RADAR point clouds. In some embodiments of the present disclosure, certain compression operations may be performed on the RADAR point clouds to reduce the amount of data that is communicated from the ego-machines to the map system.

Sensor data based map creation and localization for autonomous systems and applications

A trajectory for an autonomous machine may be evaluated for safety based at least on determining whether the autonomous machine would be capable of occupying points of the trajectory in space-time while still being able to avoid a potential future collision with one or more objects in the environment through use of one or more safety procedures. To do so, a point of the trajectory may be evaluated for conflict based at least on a comparison between points in space-time that correspond to the autonomous machine executing the safety procedure(s) from the point and arrival times of the one or more objects to corresponding position(s) in the environment. A trajectory may be sampled and evaluated for conflicts at various points throughout the trajectory. Based on results of one or more evaluations, the trajectory may be scored, eliminated from consideration, or otherwise considered for control of the autonomous machine.

Using arrival times and safety procedures in motion planning trajectories for autonomous vehicles

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

An end-to-end system for data generation, map creation using the generated data, and localization to the created map is disclosed. Mapstreams&#x2014;or streams of sensor data, perception outputs from deep neural networks (DNNs), and/or relative trajectory data&#x2014;corresponding to any number of drives by any number of vehicles may be generated and uploaded to the cloud. The mapstreams may be used to generate map data&#x2014;and ultimately a fused high definition (HD) map&#x2014;that represents data generated over a plurality of drives. When localizing to the fused HD map, individual localization results may be generated based on comparisons of real-time data from a sensor modality to map data corresponding to the same sensor modality. This process may be repeated for any number of sensor modalities and the results may be fused together to determine a final fused localization result.

Map creation and localization for autonomous driving applications

Growing community of inventors

Seattle, WA, United States of America

Birgit Henke