Rainy Environment Identification Based on Channel State Information for Autonomous Vehicles
Source abstract
Abstract
We introduce an innovative deep learning approach specifically designed for the environment identification of intelligent vehicles under rainy conditions in this paper. In the construction of wireless vehicular communication networks, an innovative approach is proposed that incorporates additional multipath components to simulate the impact of raindrop scattering on the vehicle-to-vehicle (V2V) channel, thereby emulating the channel characteristics of vehicular environments under rainy conditions and an equalization strategy in OFDM-based systems is proposed at the receiver end to counteract channel distortion. Then, a rainy environment identification method for autonomous vehicles is proposed. The core of this method lies in utilizing the Channel State Information (CSI) shared within the vehicular network to accurately identify the diverse rainy environments in which the vehicle operates without relying on traditional sensors. The environmental identification task is considered as a multi-class classification problem and a dedicated Convolutional Neural Network (CNN) model is proposed. This CNN model uses the CSI estimated from CAM exchanged in vehicle-to-vehicle (V2V) communication as training features. Simulation results showed that our method achieved an accuracy rate of 95.7% in recognizing various rainy environments, which significantly surpasses existing classical classification models. Moreover, it only took microseconds to predict with high accuracy, surpassing the performance limitations of traditional sensing systems under adverse weather conditions. This breakthrough ensures that intelligent vehicles can rapidly and accurately adjust driving parameters even in complex weather conditions like rain to autonomous drive safely and reliably.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Autonomous Vehicle Technology and Safety · Video Surveillance and Tracking Methods · Image Enhancement Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Communication and Network Laboratory, Dalian University, Dalian 116622, China
School of Information Engineering, Dalian University, Dalian 116622, China
Department of Computer Science, Loughborough University, Loughborough LE11 3TU, UK
References (34)
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