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

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.

Cited by in corpus (9)

• CNN-based Lidar Point Cloud De-Noising in Adverse Weather
• 4DenoiseNet: Adverse Weather Denoising from Adjacent Point Clouds
• Robust 3D Object Detection in Cold Weather Conditions
• DeRO: Dead Reckoning Based on Radar Odometry With Accelerometers Aided for Robot Localization
• Detecting the Anomalies in LiDAR Pointcloud
• Perception Simplex: Verifiable Collision Avoidance in Autonomous Vehicles Amidst Obstacle Detection Faults
• Detection of Condensed Vehicle Gas Exhaust in LiDAR Point Clouds
• 3D Roadway Scene Object Detection with LIDARs in Snowfall Conditions
• Detection and Tracking of MAVs Using a Rosette Scanning Pattern LiDAR