The Impact of Adverse Weather Conditions on Autonomous Vehicles: How Rain, Snow, Fog, and Hail Affect the Performance of a Self-Driving Car

TLDR

Autonomous vehicle development has attracted public attention, yet their performance degrades significantly in adverse weather such as rain, snow, fog, and hail. The article reviews how adverse weather affects key autonomous vehicle sensors, including lidar, GPS, camera, and radar. The study characterizes rainfall effects on millimeter-wave radar, analyzing both attenuation and backscatter. Simulations show that severe rainfall can cut millimeter-wave radar detection range by up to 45%, and backscatter varies markedly with target radar cross-section. No prior systematic review exists on weather effects on autonomous vehicle sensors.

Abstract

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. In this article, we first present a literature review about the impact of adverse weather conditions on state-ofthe-art sensors, such as lidar, GPS, camera, and radar. Then, we characterize the effect of rainfall on millimeter-wave (mmwave) radar, which considers both the rain attenuation and the backscatter effects. Our 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.