Making Radar Detections Safe for Autonomous Driving: A Review

Abstract

Radar sensors rank among the most common sensors used for highly automated driving functions due to their solid distance and velocity measurement capabilities and their robustness against adversarial environmental conditions. However, radar point clouds are noisy and must therefore be filtered. This work reviews current research with the aim to make radar detections usable for safe perception functions which require a guarantee for correctness of the measured environmental representation. The impact on radar errors on the distinct downstream tasks is explained. Besides, the term of safety for automated driving functions is illuminated under consideration of the current standards and state-of-the-art research interpreting these standards is presented. Furthermore, this work discusses safe radar signal processing and filtering, approaches to enrich radar data points by information fusion, e.g. from cameras and other radars, and development tools for safe radar-based perception functions. Finally, next steps on the way towards safety guarantees for radar sensors are identified.