• Login
  • Register
  • Search

Vehicle Relative Positioning System Based on Vision and V2X

Libo Cao, Leqi Zhang, Lingbo Yan, Zheng Chen, Guoliang Xiang

Abstract


Obtaining the accurate position information of the vehicle is of great significance for the intelligent driving assistance system. Due to the relative positioning accuracy based on Global Positioning System is not high enough and the signal is unstable, and the positioning system based on wireless communication needs to lay a large number of roadside units, a relative positioning method based on vision and V2X is proposed. The error distribution of the positioning method is analyzed, and the positioning result is Kalman filtering. The results show that the relative positioning system can achieve positioning accuracy within 0.38m of the smooth straight road and 0.35m of the curved and large undulating road, and its positioning stability can be improved after Kalman filtering.


Keywords


Intelligent Transportation Systems; Relative Positioning; V2X; Vision

Full Text:

PDF

References


Guan Guixia, Zhu Hong, Guan Yong, et al. GPS Positioning Error Analysis and State Estimation. Computer Engineering, 2008,34 (1): 236-238.

Jim Nzf, NARANJO J E, Garcaf, et al. Limitations of positioning systems for developing digital maps and locating vehicles according to the specifications of future driver assistance systems. IET Intelligent Transport Systems, 2011, 5(1):60-69.

Zhao Haitao, Zhu Hongbo, Liu Nanjie, et al. Vehicle Location Technology Based on Pseudorange Double Difference in Vehicle Networking. Data Acquisition and Processing, 2016,31(6): 1178-1184.

Lu Guangquan; Multi-vehicle Cooperative Positioning System Considering Relative Positions of Vehicles in Vehicle vehicle Communication Environment; C]/Wu Zhongze; Proceedings of the 12th China Intelligent Transportation Conference; Beijing; Electronic Industry Press; 2017:436-442.

Beecroft M, Mcdonald M, PIAO Ji-nan, et al. Vehicle positioning for improving road safety. Transport Reviews, 2010,30(6): 701-715.

Wang Jian. Road Survey Node Deployment and Heterogeneous Network Switching Method for Vehicle-Road Collaboration. Xi 'an: School of Information Engineering, Chang 'an University, 2015:10-22.

Liu Jianqi. Research on Wireless Ranging and Vehicle Combination Location Algorithm Based on Road Testing Equipment. Guangzhou: Guangdong University of Technology School of Automation.2016: 25-44.

Peng Yu, Wang Dan. Overview of wireless sensor network positioning technology. Journal of Electronic Measurement and Instruments, 2011, 25 (5): 389-399.

Li Zhonghai, Liu Nanjie, Bo Huang, etc. Vehicle Relative Positioning Technology Based on VANET; J. Network and Communication; 2015; 34 (19 ): 1674-7720.

Jiao L; XING J P, LI F Y. Performance Comparison of Residual Related Algorithms for TOA Positioning in Wireless Terrestrial and Sensor Networks. 1st International Conference on Wireless Communication, Vehicular Technology, Information Theory and Aerospace & Electronic Systems Technology. Copenhagen: IEEE, 2009:278-283.

Zhao Taiyang, Guo Chengan, Jin Minglu. An RFID-based traffic information acquisition system and vehicle location method. Electronics and Informatics Newspaper, 2010, 32, 11, 2612-2617.

Xu Liyuan, He Jie, Wang Ran, et al. Estimation Algorithm of Relative Positioning Coordinates for Vehicle Networking without Relying on Accurate Initial Coordinates. chinese journal of computers, 2017, 40(7):583-1597.

ALAM N, BALAEI A T, DEMPSTER A G. An instantaneous lane-level positioning using DSRC carrier frequency offset. IEEE Transactions on Intelligent Transportation Systems, 2012, 13(4):1566-1575.

Gao Jianli. Research on Vehicle Location Algorithm Based on RFID and DSP Technology. Dalian: School of Communication, Dalian University of Technology, 2008:6-20.

WANG Jian-qiang, NI Dai-heng, LI Ke-qiang, et al. RFID-Based Vehicle Positioning and Its Applications in Connected Vehicles. Sensors, 2014, 14(3): 4225-4238.

Zhou Weijie. Research on Vehicle Curve Recognition and Relative Positioning Based on Smartphone; Heilongjiang: School of Computer Science and Technology, Heilongjiang University.2015:57-67.

KIM Z W. Robust Lane Detection and Tracking in Challenging Scenarios. Intelligent Transportation Systems, IEEE Transactions on, 2008, 9(1):16-26.

Chen Xiao, Cui Xue Rong, song Hui ying, et al. research on accurate positioning method of expressway vehicles based on UWB/DGNSS. microcomputer should be Use, 2017, 33, 3, 4-6.

Koji Ishizuka, Kohei Ohno, Makoto Itami. A Study on UWB Positioning System for the Safety of Pedestrians. 16th International IEEE Annual Conference on Intelligent Transportation Systems. The Hague, The Netherlands:IEEE, 2013:2445-2450.

Chen Xiaosi, Shen Chong, Zhou Qun, et al. Research on Differential UWB Indoor Positioning System Based on TDOA Algorithm. Modern Electronic Technology, 2018.41(6):45-49.

Comrades, Zhao Tao, Holle, etc. Binocular Vision-Based Positioning and Speed Detection of Engineering Vehicles. China Mechanical Engineering, 2018, 29(4):423-428.

Zhang Yong, Tian Linya, Ma Binghao, et al. Application of Kalman Filter in GPS Precise Point Positioning. Surveying and Mapping Bulletin, 2013(7): 8-11.

Huang Li Application of Kalman Filter in Vehicle Integrated Navigation System. D. Shanghai: School of Electronic Information and Electrical Engineering, Shanghai Jiaotong University 2009:29-48.




DOI: http://dx.doi.org/10.18686/mt.v8i1.2005

Refbacks