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

Author:

Wang Jizhao1ORCID,Wu Zhizhou12,Liang Yunyi3,Tang Jinjun3ORCID,Chen Huimiao4ORCID

Affiliation:

1. School of Mechanical Engineering, Xinjiang University, Urumqi 830017, China

2. College of Transportation Engineering, Tongji University, Shanghai 201804, China

3. School of Traffic & Transportation Engineering, Central South University, Changsha 410075, China

4. Tsinghua Laboratory of Brain and Intelligence, Tsinghua University, Beijing 100084, China

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.

Funder

National Natural Science Foundation of China

Major project of new generation of artificial intelligence

Autonomous Region Postgraduate Innovation project

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry

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