TY - GEN
T1 - High-definition map combined local motion planning and obstacle avoidance for autonomous driving
AU - Jian, Zhiqiang
AU - Zhang, Songyi
AU - Chen, Shitao
AU - Lv, Xin
AU - Zheng, Nanning
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/6
Y1 - 2019/6
N2 - Local motion planning plays an important role in an autonomous driving system. And applying mature local motion planning methods to real traffic scenarios with regular constraints is one of the keys to the applications of autonomous vehicles. In this paper, we present a local motion planning method combined with High-Definition (HD) maps. Through the HD map defined by OpenStreetMap, the local motion planner can obtain the prior knowledge of traffic scenarios and achieve path planning and optimization accordingly. In order to improve the safety and comfort of the obstacle avoiding process, we also propose an inertia-like path selection algorithm based on this planning method. We evaluated the proposed method on our designed autonomous driving experimental platform 'Pioneer' and participated in the 2018 Intelligent Vehicles Future Challenge. In the competition, the 'Pioneer' successfully completed all the races and won the championship without any manual intervention.
AB - Local motion planning plays an important role in an autonomous driving system. And applying mature local motion planning methods to real traffic scenarios with regular constraints is one of the keys to the applications of autonomous vehicles. In this paper, we present a local motion planning method combined with High-Definition (HD) maps. Through the HD map defined by OpenStreetMap, the local motion planner can obtain the prior knowledge of traffic scenarios and achieve path planning and optimization accordingly. In order to improve the safety and comfort of the obstacle avoiding process, we also propose an inertia-like path selection algorithm based on this planning method. We evaluated the proposed method on our designed autonomous driving experimental platform 'Pioneer' and participated in the 2018 Intelligent Vehicles Future Challenge. In the competition, the 'Pioneer' successfully completed all the races and won the championship without any manual intervention.
UR - https://www.scopus.com/pages/publications/85072279652
U2 - 10.1109/IVS.2019.8814229
DO - 10.1109/IVS.2019.8814229
M3 - 会议稿件
AN - SCOPUS:85072279652
T3 - IEEE Intelligent Vehicles Symposium, Proceedings
SP - 2180
EP - 2186
BT - 2019 IEEE Intelligent Vehicles Symposium, IV 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 30th IEEE Intelligent Vehicles Symposium, IV 2019
Y2 - 9 June 2019 through 12 June 2019
ER -