This review is a summary of "The Impact of Adverse Weather Conditions on Autonomous Vehicles: How Rain, Snow, Fog, and Hail Affect the Performance of a Self-Driving Car" by Shizhe Zang, Ming Ding, David Smith, Paul Tyler, Thierry Rakotoarivelo, and Mohamed Ali Kaafar, published in the second quarter 2019 of the IEEE Vehicular Technology Magazine.
Recently, the development of autonomous vehicles and intelligent driver assistance systems has drawn a significant amount of attention from the general public.
One of the most critical issues in the development of autonomous vehicles and driver assistance systems is their poor performance under adverse weather conditions, such as rain, snow, fog, and hail. However, no current study provides a systematic and unified review of the effect that weather has on the various types of sensors used in autonomous vehicles
This article first presents a literature review about the impact of adverse weather conditions on state-of-the-art sensors, such as lidar, GPS, camera, and radar. Following this is a characterization of the effect of rainfall on millimeter-wave (mmwave) radar, which considers both the rain attenuation and the backscatter effects.
The simulation results show that the detection range of mm-wave radar can be reduced by up to 45% under severe rainfall conditions. Moreover, the rain backscatter effect is significantly different for targets with different radar cross-section (RCS) areas.
Full article: IEEE Vehicular Technology Magazine, Volume 14, Number 2, June 2019