Benchmarking Image Sensors Under Adverse Weather Conditions for Autonomous Driving

TL;DR

This paper presents a benchmarking methodology for evaluating image sensors in adverse weather, demonstrating that gated imaging outperforms standard imaging in foggy conditions for autonomous driving.

Contribution

It introduces a systematic testing approach to compare sensor technologies under challenging weather, exemplified by evaluating gated versus standard imaging.

Findings

Gated imaging performs better than standard imaging in foggy conditions.

The methodology enables objective comparison of sensor robustness in adverse weather.

Active illumination enhances sensor performance in challenging environments.

Abstract

Adverse weather conditions are very challenging for autonomous driving because most of the state-of-the-art sensors stop working reliably under these conditions. In order to develop robust sensors and algorithms, tests with current sensors in defined weather conditions are crucial for determining the impact of bad weather for each sensor. This work describes a testing and evaluation methodology that helps to benchmark novel sensor technologies and compare them to state-of-the-art sensors. As an example, gated imaging is compared to standard imaging under foggy conditions. It is shown that gated imaging outperforms state-of-the-art standard passive imaging due to time-synchronized active illumination.