A Benchmark for Lidar Sensors in Fog: Is Detection Breaking Down?

TL;DR

This paper evaluates the robustness of current lidar sensors in foggy conditions, revealing their vulnerabilities and exploring tuning methods to enhance detection performance in adverse weather for autonomous driving.

Contribution

It provides a comprehensive benchmark of four lidar sensors in fog, identifying key disturbance patterns and demonstrating how parameter tuning can mitigate adverse effects.

Findings

Lidar sensors' detection performance degrades significantly in fog.

Tuning internal parameters can improve lidar robustness in foggy conditions.

Identifies specific disturbance patterns affecting lidar sensors in fog.

Abstract

Autonomous driving at level five does not only means self-driving in the sunshine. Adverse weather is especially critical because fog, rain, and snow degrade the perception of the environment. In this work, current state of the art light detection and ranging (lidar) sensors are tested in controlled conditions in a fog chamber. We present current problems and disturbance patterns for four different state of the art lidar systems. Moreover, we investigate how tuning internal parameters can improve their performance in bad weather situations. This is of great importance because most state of the art detection algorithms are based on undisturbed lidar data.