Automotive Radars Principles

Autonomous driving is one of the automotive industry's key megatrends, with most car manufacturers already incorporating varying levels of autonomy into commercially available vehicles. The primary task of the sensing suite in autonomous vehicles is to provide the most reliable and dense information about the vehicle's surroundings. To achieve the required sensing performance, sensors must detect, localize, and classify a wide range of typical objects, such as vehicles, pedestrians, poles, and guardrails. Autonomous vehicles are equipped with multiple sensors of various modalities: radars, cameras, and lidars. However, lidars are costly, cameras are sensitive to illumination and weather conditions, need to be mounted behind optically transparent surfaces, and lack direct range and velocity measurement capabilities. In contrast, radars offer robustness to adverse weather conditions, are unaffected by lighting changes, provide long-range, accurate measurements, and can be installed behind nontransparent fascia.

The unique nature of automotive radar scenarios requires developing new signal-processing approaches beyond the classical military radar concepts. The redefinition of vehicular radar tasks and new performance demands offer a fertile ground for innovative signal processing techniques.

This lecture begins by outlining the radar performance requirements critical for active safety and autonomous driving and the associated challenges. It then reviews current trends in automotive radar technology and highlights the advantages of radar over other sensing modalities. The lecture explores propagation phenomena encountered in automotive radar and the radar concepts developed to address them. The radar processing chain will be discussed, including range and Doppler estimation, beamforming, detection, angle-of-arrival migration, tracking, and clustering. MIMO and cognitive radar approaches for automotive applications will be covered, followed by radar interference mitigation and sensor fusion discussions. Finally, open research questions are presented to inspire future developments in the field.

Biography:

Dr. Igal Bilik received B.Sc., M.Sc., and Ph.D. degrees in electrical and computer engineering from the Ben-Gurion University of the Negev, Beer Sheva, Israel, in 1997, 2003, and 2006, respectively. During 2006–2008, he was a postdoctoral research associate in the Department of Electrical and Computer Engineering at Duke University, Durham, NC. During 2008-2011, he was an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Massachusetts, Dartmouth. During 2011-2019, he was a Staff Researcher at GM Advanced Technical Center, Israel, leading automotive radar technology development. Between 2019 and 2020, he led the Smart Sensing and Vision Group at GM R&D, where he was responsible for developing state-of-the-art automotive radar, lidar, and computer vision technologies. Since Oct. 2020, Dr. Bilik has been an Assistant Professor in the School of Electrical and Computer Engineering at the Ben-Gurion University of the Negev. Since 2020, he has been a member of the IEEE AESS Radar Systems Panel Committee and a chair of the Civilian Radar Committee. Dr. Bilik is an Acting Officer of the IEEE Vehicular Technology Chapter in Israel and chairs the Autonomous and Connected Transportation Committee at the Israeli Center for Smart Mobility Research. He has served as an Associate Editor (AE) for the IEEE Transactions on Aerospace and Electronic Systems since 2020 and is currently a Senior Editor (SE) for these transactions. He has been an AE of the IEEE Sensors and IEEE TRS since 2022 and a Member of the Transactions on Radar Systems Editorial Committee. Dr. Bilik has more than 240 patent inventions, authored more than 90 peer-reviewed academic publications, received the Best Student Paper Awards at IEEE RADAR 2005 and IEEE RADAR 2006 Conferences, Student Paper Award in the 2006 IEEE 24th Convention of Electrical and Electronics Engineers in Israel, the GM Product Excellence Recognition in 2017, and IEEE AESS Industrial Innovation Award 2024.

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