Towards Robust Radar Perception in Autonomous Vehicles

Abstract

Autonomous driving requires reliable perception under diverse and adverse conditions. Among modern sensing modalities, automotive radar plays a unique role due to its robustness in poor weather and low visual visibility, its direct measurement of radial velocity via the Doppler effect, and its ability to detect objects beyond the line of sight. Despite these advantages, radar data are sparse, noisy, and affected by artifacts such as multipath reflections and sidelobes, resulting in weak geometric representations and high false alarm rates. Consequently, perception methods developed for cameras and lidar cannot be directly transferred to radar. This dissertation advances robust radar perception for autonomous vehicles by developing deep learning methods tailored to radar characteristics for motion estimation, multi-radar fusion, extrinsic calibration, and scene segmentation. The central research question is whether radar can be elevated from a supporting sensor to a primary perception modality capable of delivering robust and accurate information for automotive applications. To address this, the thesis investigates which tasks are best suited to radar, how deep learning can be effectively integrated with radar data, how multiple radars can be fused without strict synchronization, how extrinsic misalignment can be corrected during normal driving, and whether common perception requirements can be addressed within a unified framework.