Aptiv Technologies Limited

Aptiv Technologies Ag

John Kirkwood

Kanishka Tyagi

Narbik Manukian

Shan Zhang

Yihang Zhang

Sanling Song

This document describes techniques and systems related to a radar system using a machine-learned model for stationary object detection. The radar system includes a processor that can receive radar data as time-series frames associated with electromagnetic (EM) energy. The processor uses the radar data to generate a range-time map of the EM energy that is input to a machine-learned model. The machine-learned model can receive as inputs extracted features corresponding to the stationary objects from the range-time map for multiple range bins at each of the time-series frames. In this way, the described radar system and techniques can accurately detect stationary objects of various sizes and extract critical features corresponding to the stationary objects.

Radar system using a machine-learned model for stationary object detection

Techniques and apparatuses are described that implement object classification using low-level radar data. In particular, a radar system extracts features of a detected object based on low-level data. The radar system analyzes these features using machine learning to determine an object class associated with the detected object. By relying on low-level data, the radar system is able to extract additional information regarding the distribution of energy across range, range rate, azimuth, or elevation, which is not available in detection-level data. With the use of machine learning, the object can be classified quickly (e.g., within a single frame or observation), thereby enabling sufficient time for the autonomous-driving logic to initiate an appropriate action based on the object's class. Furthermore, this classification can be performed without the use of information from other sensors.

Object classification using low-level radar data

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John Kirkwood