Safe Autonomous Driving in Adverse Weather: Sensor Evaluation and Performance Monitoring

TL;DR

This paper evaluates radar, lidar, and camera sensors under adverse weather conditions, developing a method to detect sensor degradation and improve autonomous vehicle safety during rain, fog, day, and night.

Contribution

It introduces a sensor performance evaluation method and a degradation detection technique for autonomous vehicle sensors in adverse weather.

Findings

Sensor signals vary significantly in rain and fog.

The developed method can identify degraded sensor data areas.

Sensor monitoring enhances safety system robustness.

Abstract

The vehicle's perception sensors radar, lidar and camera, which must work continuously and without restriction, especially with regard to automated/autonomous driving, can lose performance due to unfavourable weather conditions. This paper analyzes the sensor signals of these three sensor technologies under rain and fog as well as day and night. A data set of a driving test vehicle as an object target under different weather conditions was recorded in a controlled environment with adjustable, defined, and reproducible weather conditions. Based on the sensor performance evaluation, a method has been developed to detect sensor degradation, including determining the affected data areas and estimating how severe they are. Through this sensor monitoring, measures can be taken in subsequent algorithms to reduce the influences or to take them into account in safety and assistance systems to avoid malfunctions.