Automotive Radar Operation in Multipath and NLOS Urban Scenarios

The effective operation of automotive radar systems in multipath and non-line-of-sight (NLOS) environments is essential for the success of advanced driver assistance systems (ADAS) and autonomous vehicles (AV). Automotive radars often experience challenges in dense urban settings due to the multipath propagation phenomenon, where radar signals reflect off various surfaces like buildings and other infrastructure. These reflections create ""ghost"" targets that degrade detection accuracy, increase false alarms, and lead to significant computational resource waste. Moreover, radar signals in NLOS scenarios are further complicated by indirect echoes, which can be indistinguishable from actual object reflections. Addressing these challenges requires accurate modeling of the propagation phenomena, multipath annotation, and advanced signal processing techniques.

This lecture will explore the principles of radar signal propagation in multipath and NLOS conditions, focusing on modeling and mitigating the adverse effects of multipath reflections. The use of deep learning models for enhanced radar perception will also be discussed, highlighting how these techniques can classify and mitigate multipath reflections. The lecture will discuss multipath-aware target localization and tracking methods, which can provide more robust and accurate environmental perception in urban settings. Through these advancements, radar systems can improve vehicle navigation and safety in complex environments, paving the way for more reliable autonomous vehicle operations. The lecture will present practical scenarios and demonstrate the significance of the multipath in enabling the safety of autonomous and active safety-equipped vehicles.