Deep neural network for detecting obstacle instances using radar sensors in autonomous machine applications

US12050285B2 · kind B2 · utility

Inventors

• Alexander Popov · Kirkland, US
• Nikolai Smolyanskiy · Seattle, US
• Ryan Oldja · Redmond, US
• Shane Murray · San Jose, US
• Tilman Wekel · Santa Clara, US
• David Nister · Bellevue, US
• Joachim Pehserl · Lynnwood, US
• Ruchi Bhargava · Redmond, US
• Sangmin Oh · San Jose, US

Key dates

• Filing date: Oct 28, 2022
• Grant date: Jul 30, 2024
• Expiry date: Oct 28, 2042

Classification

• Technology area (CPC G): Physics
• CPC primary: G06N20/20
• WIPO field: Computer technology
• WIPO sector: Electrical engineering

Abstract

In various examples, a deep neural network(s) (e.g., a convolutional neural network) may be trained to detect moving and stationary obstacles from RADAR data of a three dimensional (3D) space. In some embodiments, ground truth training data for the neural network(s) may be generated from LIDAR data. More specifically, a scene may be observed with RADAR and LIDAR sensors to collect RADAR data and LIDAR data for a particular time slice. The RADAR data may be used for input training data, and the LIDAR data associated with the same or closest time slice as the RADAR data may be annotated with ground truth labels identifying objects to be detected. The LIDAR labels may be propagated to the RADAR data, and LIDAR labels containing less than some threshold number of RADAR detections may be omitted. The (remaining) LIDAR labels may be used to generate ground truth data.