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This special session centers on cutting-edge advancements in automotive radar signal processing, bridging theoretical innovation with practical application in real-world scenarios. With the proliferation of automated driving systems and heightened emphasis on road safety, automotive radar has emerged as a cornerstone technology, offering unparalleled reliability and accuracy in adverse conditions. The proposed session will highlight pioneering research contributions, including:
- Beamspace Tensor Processing for Automotive Radar
- Realistic Micro-Doppler Simulation of Pedestrians in Automotive Radar Applications
- Range-Doppler Imaging Using One-Bit PMCW Radar with Neural Network
- Radar Tracking Enhancement Utilizing Target's Depth Information Extracted from RD-Map
- A Tight Multiple-Source Partial Relaxation-Based Technique for Automotive MIMO Radar Imaging
By addressing diverse challenges such as micro-Doppler simulation, angular resolution enhancements, computational efficiency, and novel radar imaging techniques, these contributions collectively aim to advance the reliability and functionality of radar systems essential for ADAS and autonomous driving.