Object detection for automotive radar point clouds – a comparison

• Published in: AI perspectives 2021-11, Vol.3 (1), Article 6
• Main Authors: Scheiner, Nicolas, Kraus, Florian, Appenrodt, Nils, Dickmann, Jürgen, Sick, Bernhard
• Format: Article
• Language: English
• Subjects: Algorithms; Automotive radar; Domains; Engineering; Lidar; Machine learning; Nexrad; Object recognition; Radar data; Radar detection; Radar equipment; Research Article; Sensors; Weather
• ISSN: 2523-398X; 2523-398X
• DOI: 10.1186/s42467-021-00012-z

Automotive radar perception is an integral part of automated driving systems. Radar sensors benefit from their excellent robustness against adverse weather conditions such as snow, fog, or heavy rain. Despite the fact that machine-learning-based object detection is traditionally a camera-based domain, vast progress has been made for lidar sensors, and radar is also catching up. Recently, several new techniques for using machine learning algorithms towards the correct detection and classification of moving road users in automotive radar data have been introduced. However, most of them have not been compared to other methods or require next generation radar sensors which are far more advanced than current conventional automotive sensors. This article makes a thorough comparison of existing and novel radar object detection algorithms with some of the most successful candidates from the image and lidar domain. All experiments are conducted using a conventional automotive radar system. In addition to introducing all architectures, special attention is paid to the necessary point cloud preprocessing for all methods. By assessing all methods on a large and open real world data set, this evaluation provides the first representative algorithm comparison in this domain and outlines future research directions.