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
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Reference143 articles.
1. Gerla, M., Lee, E.-K., Pau, G., and Lee, U. (2014, January 6–8). Internet of vehicles: From intelligent grid to autonomous cars and vehicular clouds. Proceedings of the 2014 IEEE World Forum on Internet of Things (WF-IoT), Seoul, Republic of Korea. 2. Behere, S., and Törngren, M. (2015, January 4). A functional architecture for autonomous driving. Proceedings of the First International Workshop on Automotive Software Architecture, Montreal, QC, Canada. 3. Yan, Z., Li, P., Fu, Z., Xu, S., Shi, Y., Chen, X., Zheng, Y., Li, Y., Liu, T., and Li, C. (2023, January 2–6). INT2: Interactive Trajectory Prediction at Intersections. Proceedings of the IEEE/CVF International Conference on Computer Vision, Paris, France. 4. A co-evolutionary lane-changing trajectory planning method for automated vehicles based on the instantaneous risk identification;Wu;Accid. Anal. Prev.,2023 5. Load frequency control of power system considering electric Vehicles’ aggregator with communication delay;Tripathi;Int. J. Electr. Power Energy Syst.,2023
|
|