TY - GEN
T1 - 3D-LIDAR based branch estimation and intersection location for autonomous vehicles
AU - Wang, Liang
AU - Wang, Jun
AU - Wang, Xiaonian
AU - Zhang, Yihuan
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/28
Y1 - 2017/7/28
N2 - It is crucial for autonomous vehicles to navigate at intersections. The accurate location of intersections and the orientation of each branches are necessary for decision making and path planning. In this paper, a unified method is proposed to estimate orientations of each branch at an intersection and to locate the position of the intersection. First, based on vehicle dynamics, a densifying method is used to obtain dense point-cloud data using 3D-LIDAR sensor. Then, according to the data of Open Street Map, the regions-of-interest are extracted and the points are interpolated to transform into an elevation image. Finally, a support vector regression model is employed to estimate the position and orientation of each branch and a fusion method is used to locate the intersection. The experimental results demonstrate the accuracy and robustness of the proposed algorithm.
AB - It is crucial for autonomous vehicles to navigate at intersections. The accurate location of intersections and the orientation of each branches are necessary for decision making and path planning. In this paper, a unified method is proposed to estimate orientations of each branch at an intersection and to locate the position of the intersection. First, based on vehicle dynamics, a densifying method is used to obtain dense point-cloud data using 3D-LIDAR sensor. Then, according to the data of Open Street Map, the regions-of-interest are extracted and the points are interpolated to transform into an elevation image. Finally, a support vector regression model is employed to estimate the position and orientation of each branch and a fusion method is used to locate the intersection. The experimental results demonstrate the accuracy and robustness of the proposed algorithm.
UR - http://www.scopus.com/inward/record.url?scp=85028069796&partnerID=8YFLogxK
U2 - 10.1109/IVS.2017.7995912
DO - 10.1109/IVS.2017.7995912
M3 - Conference contribution
AN - SCOPUS:85028069796
T3 - IEEE Intelligent Vehicles Symposium, Proceedings
SP - 1440
EP - 1445
BT - IV 2017 - 28th IEEE Intelligent Vehicles Symposium
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 28th IEEE Intelligent Vehicles Symposium, IV 2017
Y2 - 11 June 2017 through 14 June 2017
ER -