TY - GEN
T1 - A positioning algorithm of autonomous car based on map-matching and environmental perception
AU - Xu, Qian
AU - Wang, Meiling
AU - Du, Zhifang
AU - Zhang, Yi
N1 - Publisher Copyright:
© 2014 TCCT, CAA.
PY - 2014/9/11
Y1 - 2014/9/11
N2 - Autonomous car is an important tool for transportation and military in the future, and its precise positioning is the basis of autonomous navigation. Most positioning algorithms based on map-matching for autonomous car make little use of environmental perception information. To solve this problem, a positioning algorithm is proposed in this paper, which is based on map-matching and environmental perception for autonomous car. The algorithm includes macroscopic road matching and microscopic precise positioning. As for macroscopic road matching, the algorithm makes use of computational geometry to match the position of autonomous car to the corresponding road, based on GPS point and map information of the road network. As for microscopic precise positioning, the algorithm makes use of the environmental perception, which is detected by the autonomous car to make precise positioning. Macroscopic road matching provides matching road to microscopic precise positioning, and microscopic precise positioning eliminates gross error produced in macroscopic road matching. Through real car tests, the algorithm can match map quickly, improving the positioning precision with strong real-time.
AB - Autonomous car is an important tool for transportation and military in the future, and its precise positioning is the basis of autonomous navigation. Most positioning algorithms based on map-matching for autonomous car make little use of environmental perception information. To solve this problem, a positioning algorithm is proposed in this paper, which is based on map-matching and environmental perception for autonomous car. The algorithm includes macroscopic road matching and microscopic precise positioning. As for macroscopic road matching, the algorithm makes use of computational geometry to match the position of autonomous car to the corresponding road, based on GPS point and map information of the road network. As for microscopic precise positioning, the algorithm makes use of the environmental perception, which is detected by the autonomous car to make precise positioning. Macroscopic road matching provides matching road to microscopic precise positioning, and microscopic precise positioning eliminates gross error produced in macroscopic road matching. Through real car tests, the algorithm can match map quickly, improving the positioning precision with strong real-time.
KW - Autonomous car
KW - Computational geometry
KW - Environmental perception
KW - Map-matching
UR - http://www.scopus.com/inward/record.url?scp=84907924738&partnerID=8YFLogxK
U2 - 10.1109/ChiCC.2014.6896712
DO - 10.1109/ChiCC.2014.6896712
M3 - Conference contribution
AN - SCOPUS:84907924738
T3 - Proceedings of the 33rd Chinese Control Conference, CCC 2014
SP - 707
EP - 712
BT - Proceedings of the 33rd Chinese Control Conference, CCC 2014
A2 - Xu, Shengyuan
A2 - Zhao, Qianchuan
PB - IEEE Computer Society
T2 - Proceedings of the 33rd Chinese Control Conference, CCC 2014
Y2 - 28 July 2014 through 30 July 2014
ER -