TY - GEN
T1 - 3D LIDAR-Based Intersection Recognition and Road Boundary Detection Method for Unmanned Ground Vehicle
AU - Zhang, Yihuan
AU - Wang, Jun
AU - Wang, Xiaonian
AU - Li, Chaocheng
AU - Wang, Liang
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/10/30
Y1 - 2015/10/30
N2 - Environment perception is an essential component in autonomous driving technology. Curb is one of the most prominent features on an urban road, which defines the boundary of a road surface. An intersection is a junction of two or more roads, appearing where no curb exists. Intersection recognition and curb detection are significant in environment perception which ensure the performance of Unmanned Ground Vehicles (UGVs). In this paper, a novel double layer beam model is proposed to recognise the intersection shape and classify the road type in front of the UGV, and then based on the current road type and spatial features, a real-time road boundary detection algorithm is proposed to extract the curb position. The performance of the proposed method is verified through extensive experiments with a UGV autonomously driving on campus roads. The experimental results demonstrate the accurate and robust performance of the proposed algorithm.
AB - Environment perception is an essential component in autonomous driving technology. Curb is one of the most prominent features on an urban road, which defines the boundary of a road surface. An intersection is a junction of two or more roads, appearing where no curb exists. Intersection recognition and curb detection are significant in environment perception which ensure the performance of Unmanned Ground Vehicles (UGVs). In this paper, a novel double layer beam model is proposed to recognise the intersection shape and classify the road type in front of the UGV, and then based on the current road type and spatial features, a real-time road boundary detection algorithm is proposed to extract the curb position. The performance of the proposed method is verified through extensive experiments with a UGV autonomously driving on campus roads. The experimental results demonstrate the accurate and robust performance of the proposed algorithm.
UR - http://www.scopus.com/inward/record.url?scp=84950291191&partnerID=8YFLogxK
U2 - 10.1109/ITSC.2015.88
DO - 10.1109/ITSC.2015.88
M3 - Conference contribution
AN - SCOPUS:84950291191
T3 - IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
SP - 499
EP - 504
BT - Proceedings - 2015 IEEE 18th International Conference on Intelligent Transportation Systems
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 18th IEEE International Conference on Intelligent Transportation Systems, ITSC 2015
Y2 - 15 September 2015 through 18 September 2015
ER -