Abstract:In order to determine the problem of vehicle positioning deviation caused by a lack and delay of the positioning signal,a cooperative map-matching(CMM)algorithm,based on the cooperative vehicle infrastructure system,is proposed in this paper. First,the information obtained by GPS and that obtained by vehicular dead reckoning(DR)were fused to obtain the initial position of cooperative map-matching using an extended Kalman filter(EKF). Then,vehicle information was exchanged and shared based on dedicated short-range communication (DSRC). On the basis of an electronic map,the further positioning of vehicles was accomplished using road constraints. In order to verify the effectiveness of the proposed algorithm,an environment to simulate real scenes was set up to conduct the experiments. The experimental results demonstrate that the average positioning deviation of vehicles at intersections using EKF,which fuses data obtained from GPS and DR,is 9.90 m. The positioning deviation decreased by 30.87%,when compared with the average deviation of GPS,which is 14.31 m. The proposed CMM algorithm has an average position deviation of 4.5 m when the number of vehicles involved is 7,and 2.75 m when the number of vehicles involved is 10. The positioning deviation decreased by 69.74%.
HUANG Chungming, LIN Shihyang. Cooperative vehicle collision warning system using the vector-based approach with dedicated short range communication data transmission[J]. IET Intelligent Transport Systems, 2014, 8(2):124-134
[2]
吴国锋. 蜂拥算法及其在协同自动驾驶中的应用[D]. 成都:电子科技大学,2015
[3]
VAN A B, VAN D C J G, VISSER R. The impact of cooperative adaptive cruise control on traffic-flow characteristics[J]. IEEE Transactions on Intelligent Transportation Systems, 2006, 7(4):429-436
[4]
STEVENS A,BRUSQUE C,KREMS J. Driver adaptation to information and assistance systems[M].London:the Institution of Engineering and Technology,2014:319-334
[5]
ROHANI M, GINGRAS D, GRUYER D. A novel approach for improved vehicular positioning using cooperative map matching and dynamic base station DGPS concept[J]. IEEE Transactions on Intelligent Transportation Systems, 2016, 17(1):230-239
[6]
MILANES V, SHLADOVER S E, SPRING J, et al. Cooperative adaptive cruise control in real traffic situations[J]. IEEE Transactions on Intelligent Transportation Systems, 2014, 15(1):296-305
[7]
LIU K, LIM H B, FRAZZOLI E, et al. Improving positioning accuracy using GPS pseudorange measurements for cooperative vehicular localization[J]. IEEE Transactions on Vehicular Technology, 2014, 63(6):2544-2556
[8]
GOLESTAN K, SATTAR F, KARRAY F, et al. Localization in vehicular ad hoc networks using data fusion and V2V communication[J]. Computer Communications, 2015, 71(1):61-72
[9]
YAO J, BALAEI A T, HASSAN M, et al. Improving cooperative positioning for vehicular networks[J]. IEEE Transactions on Vehicular Technology, 2011, 60(6):2810-2823
[10]
ALAM N, KEALY A, DEMPSTER A G. An INS-aided tight integration approach for relative positioning enhancement in VANETs[J]. IEEE Transactions on Intelligent Transportation Systems, 2013, 14(4):1992-1996
[11]
YAO Yiqing, XU Xiaosu, ZHU Chenchen, et al. A hybrid fusion algorithm for GPS/INS integration during GPS outages[J]. Measurement, 2017, 103(1):42-51
[12]
徐宏宇,王浩,王尔申. 基于扩展卡尔曼滤波的GPS定位数据处理方法研究[J]. 科学技术与工程,2012,12(31):8137-8142 XU Hongyu, WANG Hao, WANG Ershen. Research of GPS positioning data processing based on extended kalman filtering[J]. Science Technology and Engineering, 2012, 12(31):8137-8142
[13]
SELLOUM A,BETAILLE D,LE C E,et al. Lane level positioning using particle filtering[C]//12th International IEEE conference on Intelligent Transportation Systems. St Louis:Institute of Electrical and Electronics Engineers Inc., 2009:539-544