Effect of Fog Particle Size Distribution on 3D Object Detection Under Adverse Weather Conditions

TL;DR

This study investigates how fog particle size distributions affect 3D object detection accuracy in autonomous vehicles using LiDAR, highlighting the impact of fog characteristics on detection performance across different object types and difficulties.

Contribution

It introduces a novel analysis of fog particle size effects on LiDAR-based detection, employing Mie theory and meteorological models to enhance understanding under adverse weather conditions.

Findings

Car detection accuracy reaches around 99%.

Pedestrian detection accuracy drops to about 73%.

Fog characteristics significantly influence detection performance.

Abstract

LiDAR-based sensors employing optical spectrum signals play a vital role in providing significant information about the target objects in autonomous driving vehicle systems. However, the presence of fog in the atmosphere severely degrades the overall system's performance. This manuscript analyzes the role of fog particle size distributions in 3D object detection under adverse weather conditions. We utilise Mie theory and meteorological optical range (MOR) to calculate the attenuation and backscattering coefficient values for point cloud generation and analyze the overall system's accuracy in Car, Cyclist, and Pedestrian case scenarios under easy, medium and hard detection difficulties. Gamma and Junge (Power-Law) distributions are employed to mathematically model the fog particle size distribution under strong and moderate advection fog environments. Subsequently, we modified the KITTI dataset based on the backscattering coefficient values and trained it on the PV-RCNN++ deep neural network model for Car, Cyclist, and Pedestrian cases under different detection difficulties. The result analysis shows a significant variation in the system's accuracy concerning the changes in target object dimensionality, the nature of the fog environment and increasing detection difficulties, with the Car exhibiting the highest accuracy of around 99% and the Pedestrian showing the lowest accuracy of around 73%.