Perception Methods for Adverse Weather Based on Vehicle Infrastructure Cooperation System: A Review

Abstract

Environment perception plays a crucial role in autonomous driving technology. However, various factors such as adverse weather conditions and limitations in sensing equipment contribute to low perception accuracy and a restricted field of view. As a result, intelligent connected vehicles (ICVs) are currently only capable of achieving autonomous driving in specific scenarios. This paper conducts an analysis of the current studies on image or point cloud processing and cooperative perception, and summarizes three key aspects: data pre-processing methods, multi-sensor data fusion methods, and vehicle–infrastructure cooperative perception methods. Data pre-processing methods summarize the processing of point cloud data and image data in snow, rain and fog. Multi-sensor data fusion methods analyze the studies on image fusion, point cloud fusion and image-point cloud fusion. Because communication channel resources are limited, the vehicle–infrastructure cooperative perception methods discuss the fusion and sharing strategies for cooperative perception information to expand the range of perception for ICVs and achieve an optimal distribution of perception information. Finally, according to the analysis of the existing studies, the paper proposes future research directions for cooperative perception in adverse weather conditions.

PAFNet: Pillar Attention Fusion Network for Vehicle–Infrastructure Cooperative Target Detection Using LiDAR

“…The data collected by roadside LiDAR and vehicle-side LiDAR can be symmetrically fused with each other, enhancing the perception capabilities of autonomous vehicles. The fusion of point cloud data from both the vehicle and road with spatial diversity can greatly enhance the target detection accuracy for autonomous vehicles [25]. When it comes to vehicle-infrastructure cooperative (VIC) [26] traffic sensing, there are three schemes of fusion target detection methods for roadside LiDAR and automotive LiDAR data, depending on the fusion data: early fusion [27], feature fusion [28], and late fusion [29].…”