Abstract
Forward Collision Warning (FCW) system can automatically measure the distance between obstacles and provide early warning, which can effectively provide safety guarantee for vehicle travel and reduce the probability of traffic accidents. The vision-based approaches are always popular because vision sensors have the characteristics of low cost and rich image information. In this paper, a vision-based forward collision warning method is proposed. The method contains three main stages: (1) detect obstacle based on multi-feature fusion using convolution neural networks (CNN), (2) estimate the relative distance, relative velocity from vehicles and collision time, (3) design obstacles hazard level discrimination strategy for different road scenarios. The algorithm is tested on our own dataset and the experiment results showed that the method has good feasibility and robustness.
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References
Wang SC (2012) Development and experimental verification of the decision algorithm of the front anti-collision early warning system. Doctor, Jilin university
Chang JW, Kang SJ (2018) Real-time vehicle detection and tracking algorithm for forward vehicle collision warning. J Semicond Technol Sci 18(5):547–559
Lim Q, He Y, Tan U (2018) Real-time forward collision warning system using nested kalman filter for monocular camera. In: Proceedings of the 2018 IEEE international conference on robotics and biomimetics, 12–15 December, Kuala Lumpur, Malaysia
Redmon J, Farhadi A (2018) YOLOv3: an incremental improvement. https://pjreddie.com/darknet/yolo/
Liu HZ, Yuan JZ, Zheng YR (2015) Computer vision algorithms and intelligent vehicle applications. Publishing House of Electronics Industry, Beijing
Yu GZ (2018) A forward object lateral distance calibration method based on monocular camera, China: CN109087361A
Yu GZ, Wang ZY et al (2018) Efficient lane detection using deep lane feature extraction method. SAE Int J Passeng Cars Electron Electr Syst 1(11):57–66
Hu CW, Wang YP, Yu GZ, Wang ZY et al (2018) Embedding CNN-based fast obstacles detection for autonomous vehicles. SAE Technical Paper
Acknowledgments
This work was supported by the National Key Research and Development Program of China (2016YFB0101001), the Beijing Municipal Science and Technology Project under Grant # Z181100008918003 and the Beijing Municipal Science and Technology Project under Grant #D171100005117001. The authors would also like to thank the insightful and constructive comments from anonymous reviewers.
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Zhang, Y., Wang, Z., Zhou, B., Yu, G., Hu, C., Zhang, L. (2020). A Vision-Based Method for Vehicle Forward Collision Warning. In: Jia, Y., Du, J., Zhang, W. (eds) Proceedings of 2019 Chinese Intelligent Systems Conference. CISC 2019. Lecture Notes in Electrical Engineering, vol 594. Springer, Singapore. https://doi.org/10.1007/978-981-32-9698-5_55
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DOI: https://doi.org/10.1007/978-981-32-9698-5_55
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