The ongoing automation of driving functions in cars results in the evolution of advanced driver assistance systems (ADAS) into ones capable of highly/fully automated driving in future. Radar has emerged as a core technology for sensing and perception of a vehicle’s surroundings to facilitate diverse driver assistance functions. With growing levels of automation, there is a need for high-performance radar sensing, especially with respect to angular resolution. To achieve this, advanced waveform designs and signal processing algorithms such as digital waveforms, MIMO radar, high-resolution DoA and sparse radar signal processing, and cognitive, adaptive radar approaches are needed. Furthermore, machine learning (ML)-powered radar systems are still in an early stage of development. Advances are required to design reliable, robust and explainable radar perception for real-time, safety-critical driving applications. The proposed workshop is aimed at providing a spotlight on the diverse signal processing and ML challenges in advancing automotive radar technology.

The workshop will be a mix of peer-reviewed technical papers and invited speakers from academia/industry covering a diversity of topics in automotive radar systems.

We are seeking high-quality paper submissions covering the following topics of interest:

• Automotive Radar Waveform Design

• DoA Estimation in Automotive Radars

• Automotive Imaging Radars

• Automotive Radar Interference-handling Methods

• Radar Point Cloud Processing Techniques

• Automotive Radar Fusion

• Cognitive Automotive Radar Methods

• Automotive Joint Radar-Communication Systems

• ML Methods for Automotive Radar Applications

• ML Hardware for Automotive Radars