TY - GEN
T1 - A robust submap-based road shape estimation via iterative Gaussian process regression
AU - Wang, Di
AU - Xue, Jianru
AU - Cui, Dixiao
AU - Zhong, Yang
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/28
Y1 - 2017/7/28
N2 - Road shape estimation is important for the safe driving of intelligent vehicles. The common road shape models such as line/parabola, spline and clothoid are lacking of flexibility in various urban traffic scenes. In this paper, a robust road shape model which consists of multiple overlapped submaps is proposed. Each individual submap is represented by a smooth curve generated through Gaussian process(GP). To estimate parameters of a GP submap, a framework involving pre-processing, pose correction, road shape regression and map updating/creating is proposed. Pose correction is achieved by fusion of vehicle motion model and simplified GP-based observation model. Road shape regression is used to extract a coarse road shape. Map updating/creating is used to adapt to the new coming data and generates refined road shape. A robust iterative Gaussian process regression(iGPR) is utilized in both road shape regression and map updating/creating. Extensive experimental results show the efficiency of the proposed method.
AB - Road shape estimation is important for the safe driving of intelligent vehicles. The common road shape models such as line/parabola, spline and clothoid are lacking of flexibility in various urban traffic scenes. In this paper, a robust road shape model which consists of multiple overlapped submaps is proposed. Each individual submap is represented by a smooth curve generated through Gaussian process(GP). To estimate parameters of a GP submap, a framework involving pre-processing, pose correction, road shape regression and map updating/creating is proposed. Pose correction is achieved by fusion of vehicle motion model and simplified GP-based observation model. Road shape regression is used to extract a coarse road shape. Map updating/creating is used to adapt to the new coming data and generates refined road shape. A robust iterative Gaussian process regression(iGPR) is utilized in both road shape regression and map updating/creating. Extensive experimental results show the efficiency of the proposed method.
UR - https://www.scopus.com/pages/publications/85028074469
U2 - 10.1109/IVS.2017.7995964
DO - 10.1109/IVS.2017.7995964
M3 - 会议稿件
AN - SCOPUS:85028074469
T3 - IEEE Intelligent Vehicles Symposium, Proceedings
SP - 1776
EP - 1781
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 -