2021 IEEE International Conference on Acoustics, Speech and Signal Processing
6-11 June 2021 • Toronto, Ontario, Canada
Extracting Knowledge from Information
| Paper ID | SS-12.3 | ||
| Paper Title | A NEW AUTOMOTIVE RADAR 4D POINT CLOUDS DETECTOR BY USING DEEP LEARNING | ||
| Authors | Yuwei Cheng, Tsinghua University, China; Jingran Su, Northwestern Polytechnical University, China; Hongyu Chen, Yimin Liu, Tsinghua University, China | ||
| Session | SS-12: Recent Advances in mmWave Radar Sensing for Autonomous Vehicles | ||
| Location | Gather.Town | ||
| Session Time: | Thursday, 10 June, 15:30 - 16:15 | ||
| Presentation Time: | Thursday, 10 June, 15:30 - 16:15 | ||
| Presentation | Poster | ||
| Topic | Special Sessions: Recent Advances in mmWave Radar Sensing for Autonomous Vehicles | ||
| IEEE Xplore Open Preview | Click here to view in IEEE Xplore | ||
| Abstract | The millimeter-wave radar, as an important sensor, is widely used in autonomous driving. In recent years, to meet the requirement of high level autonomous driving applications, attentions have been paid to generate high-quality radar point clouds. However, in the complex roadway environment, the weaknesses of classical radar detectors are exposed, such as too much clutter points and sparse valid point clouds. Therefore, in this paper, we propose a new automotive radar detector based on deep learning using the spatial distribution feature of the real targets, in order to improve the performance of automotive radar detector in the real-world driving scene. Besides, aiming at the lack of radar data labels, we propose an autonomous labeling method by using synchronized Lidar data. Finally, we evaluate the detector on data collected in the real-world roadway scene and the result shows that the proposed radar detector out-performs the classical radar detectors in suppressing the clutter and generating denser point clouds. | ||