Front vehicle detection based on multi-sensor information fusion

被引：0
|
作者：
Jia P. [1 ]
Liu Q. [1 ]
Peng K. [2 ]
Li Z. [1 ]
Wang Q. [1 ]
Hua Y. [1 ]
机构：
[1] CATARC (Tianjin) Automotive Engineering Research Institute Co., Ltd., Tianjin
[2] School of Mechanical Engineering, Hebei University of Technology, Tianjin
来源：
Hongwai yu Jiguang Gongcheng/Infrared and Laser Engineering | 2022年 / 51卷 / 06期
关键词：
advanced driving assisted system; camera; information fusion; millimeter wave radar;
D O I：
10.3788/IRLA20210446
中图分类号：
学科分类号：
摘要：
In order to improve the ability of Advanced Driving Assisted System (ADAS) to perceive vehicles in the road environment, the information fusion algorithm of machine vision and millimeter wave radar was proposed to detect front vehicles in this paper. Firstly, the camera and millimeter wave radar were jointly calibrated to determine their conversion formula using the coordinate measuring machine in the fusion system. The candidate frame of SSD for deep learning algorithm was optimized to improve the speed of vehicle detection, while long focus camera and short focus camera were selected for two front images acquisition, the overlapped images were fused to improve sharpness of small target image ahead. The appropriate threshold parameters of radar data were determined by radar simulator and the effective vehicle target was extracted. According to these effective target data, the image collected by the camera was selected and the region of interest was established. Vehicles in the selection region were detected with the improved SSD algorithm. In the test, the vehicle detection rate is 95.3%, and the total processing time for single frame image is 32 ms. It proves that the algorithm can help ADAS system to archieve vehicle detetcion with higher real-time and environmental adaptability. © 2022 Chinese Society of Astronautics. All rights reserved.
引用
收藏
相关论文
共 13 条
  • [1] Luo X, Wang P, Li S, Et al., Dynamic target detection method in motor vehicle assisted driving system, Computer Engineering, 44, 1, pp. 311-316, (2018)
  • [2] Wang T, Xin J, Zheng N., A method integrating human visual attention and consciousness of radar and vision for autonomous vehicle navigation, IEEE Transactions on Pattern Analysis & Machine Intelligence, 39, 6, pp. 1137-1149, (2015)
  • [3] Song W, Yang Y, Fu M., Real-time obstacles detection and status classification for collision warning in a vehicle active safety system, IEEE Transactions on Intelligent Transportation Systems, 19, 3, pp. 758-773, (2018)
  • [4] Natnael S, Yougmin W, Jintae K., Millimeter-wave radar and RGB-D camera sensor fusion for real-time people dection and tracking, International Conference on Robot Intelligence Technology and Applications(RiTA), pp. 93-98, (2019)
  • [5] Li P, Peng J C, Huang P, Et al., Condition monitoring for bogie frame based on two-objective sensor distribution optimization, China Measurement & Test, 46, 9, pp. 131-147, (2020)
  • [6] Gu G, Xu X D., Real-time vehicle detection and information recognition technology based on YOLOv3 improved algorithm, Computer Engineering and Applications, 56, 22, pp. 173-184, (2020)
  • [7] Liu K J, Ma R S, Tang Z M, Et al., Design of infrared target recognition system with optimized convolutional network [J], Optics and Precision Engineering, 29, 4, pp. 822-831, (2021)
  • [8] Zhuang Z B, Qiu Y H, Lin J Q, Et al., Turbulence warning based on convolutional neural network by lidar, Infrared and Laser Engineering, 51, 4, (2021)
  • [9] Yu R W, Han X, Du L Q., Target part recognition based inception-SSD algorithm, Optics and Precision Engineering, 28, 8, pp. 1799-1809, (2020)
  • [10] Li Y, Di J L, Ren L., Deep-learning-based prediction of living cells mitosis via quantitative phase microscopy, Chinese Optics Letters, 19, 5, (2021)
12