Research on Key Technology of Auto-driving Based on Machine Vision
Pages 429 - 433
Abstract
Aiming at the problem of vehicle travel control in high-speed automatic driving technology based on machine vision, a vehicle travel control method based on visual guidance was proposed. The method directly obtained the vehicle movement track by analytical method, and the parking control quantity could be given accurately. In addition, a lane line detection method was proposed. The detection method mainly consists of image light intensity preprocessing, segmentation and line fitting. The analysis of driving test data shows that the proposed method can detect lane lines quickly and accurately, and the automatic driving control system works smoothly and smoothly.
References
[1]
Han X M, Bao H, Liang J, Pan F and Xuan Z X. 2018. An adaptive cruise control algorithm based on deep reinforcement learning. Computer Engineering,44( 7) : 32-35,41.
[2]
Pan F and Bao H. 2021. Research progress of automatic driving control technology based on reinforcement learning. Journal of Image and Graphics, 26( 01) : 0028-0035.
[3]
SHAO Yiming, CHEN Yawei, SHU Haibo. Trajectory Tracking Control of Self-Driving Cars. JOURNAL OF CHONGQING JIAOTONG UNIVERSITY( NATURAL SCIENCE), 2019,38(8), 1-6.
[4]
HU Xiao⁃xia, LI Xue⁃mei, BAI Yan⁃yan, LI Rui. Research on driverless lane line detection. Electronic Design Engineering,2020,28(18):118-121.
[5]
DU Xuejing, ZHANG Meiou, Based on Fusing Optical Flow Method and Background Modeling for Lane Line Recognition. Journal of Chongqing University of Technology (Natural Science),2021,35(3):29-35.
[6]
LUO Jihuan, LAN Fengchong, CHEN Jiqing, A Lane Line Detection Method Combining Image Processing with Target Constraint Range Algorithm[J]. Chinese Journal of Automative Engineering,2019,9(1):27-35.
[7]
LI Yongfeng, DING Runtao, GUO Chengming. An Improved Vector Median Filtering Algorithm Based on Color Image Filtering. Journal of Data Acquisition and Processing, 2003, 18(3): 361-364.
[8]
HU Zhengyun, TONG Qiuhong, LIU Shuai. Lane Departure Identification and Early Warning Method for Autonomous Vehicle. JOU༲NAL OF CHONGQING JIAOTONG UNIVERSITY( NATURAL SCIENCE),2020,39(10):118-125.
[9]
LI Dongliang. Study on Lane Mark Recognition in Lane Departure Warning. Jinzhou: Liaoning University of Technology, 2018.
[10]
ZHANG Chong, FAN Xinnan. Approach for real-time lane detection based on linear model. Computer Engineering and Design, 2012, 33( 1) : 295-299.
[11]
WANG Heng, MA Hai-bo, XU Hai-li, HUA Guo-ran. Remaining Life Prediction Method of Mechanical Equipment Based on K-S Test and Dynamic Grey Model. Instrument Technique and Sensor, 2015,1:97-100.
Index Terms
- Research on Key Technology of Auto-driving Based on Machine Vision
Index terms have been assigned to the content through auto-classification.
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Published In
December 2021
437 pages
ISBN:9781450384155
DOI:10.1145/3507548
Copyright © 2021 ACM.
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Association for Computing Machinery
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Published: 09 March 2022
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- Refereed limited
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CSAI 2021
CSAI 2021: 2021 5th International Conference on Computer Science and Artificial Intelligence
December 4 - 6, 2021
Beijing, China
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