Forward Collision Warning Strategy Based on Millimeter-Wave Radar and Visual Fusion
Author:
Sun Chenxu1, Li Yongtao1, Li Hanyan2, Xu Enyong3, Li Yufang3, Li Wei3
Affiliation:
1. School of Mechanical and Automotive Engineering, Guangxi University of Science and Technology, Liuzhou 545616, China 2. School of Automation, Guangxi University of Science and Technology, Liuzhou 545616, China 3. Dongfeng Liuzhou Motor Company, Liuzhou 545616, China
Abstract
Forward collision warning (FCW) is a critical technology to improve road safety and reduce traffic accidents. However, the existing multi-sensor fusion methods for FCW suffer from a high false alarm rate and missed alarm rate in complex weather and road environments. For these issues, this paper proposes a decision-level fusion collision warning strategy. The vision algorithm and radar tracking algorithm are improved in order to reduce the false alarm rate and omission rate of forward collision warning. Firstly, this paper proposes an information entropy-based memory index for an adaptive Kalman filter for radar target tracking that can adaptively adjust the noise model in a variety of complex environments. Then, for visual detection, the YOLOv5s model is enhanced in conjunction with the SKBAM (Selective Kernel and Bottleneck Attention Mechanism) designed in this paper to improve the accuracy of vehicle target detection. Finally, a decision-level fusion warning fusion strategy for millimeter-wave radar and vision fusion is proposed. The strategy effectively fuses the detection results of radar and vision and employs a minimum safe distance model to determine the potential danger ahead. Experiments are conducted under various weather and road conditions, and the experimental results show that the proposed algorithm reduces the false alarm rate by 11.619% and the missed alarm rate by 15.672% compared with the traditional algorithm.
Funder
National Natural Science Foundation of China Guangxi Science and Technology Plan Project Liuzhou Science and Technology Plan Project
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
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