TY - GEN
T1 - Vision-Based Trajectory Planning via Imitation Learning for Autonomous Vehicles
AU - Cai, Peide
AU - Sun, Yuxiang
AU - Chen, Yuying
AU - Liu, Ming
N1 - Funding Information:
This work was supported by the National Natural Science Foundation of China (Grant No. U1713211); the Research Grant Council of Hong Kong SAR Government, China, under Project No. 11210017, and No. 21202816, awarded to Prof. Ming Liu.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - Reliable trajectory planning like human drivers in real-world dynamic urban environments is a critical capability for autonomous driving. To this end, we develop a vision and imitation learning-based planner to generate collision-free trajectories several seconds into the future. Our network consists of three sub-networks to conduct three basic driving tasks: keep straight, turn left and turn right. During the planning process, high-level commands are received as prior information to select a specific sub-network. We create our dataset from the Robotcar dataset, and the experimental results suggest that our planner is able to reliably generate trajectories in various driving tasks, such as turning at different intersections, lane-keeping on curved roads and changing lanes for collision avoidance.
AB - Reliable trajectory planning like human drivers in real-world dynamic urban environments is a critical capability for autonomous driving. To this end, we develop a vision and imitation learning-based planner to generate collision-free trajectories several seconds into the future. Our network consists of three sub-networks to conduct three basic driving tasks: keep straight, turn left and turn right. During the planning process, high-level commands are received as prior information to select a specific sub-network. We create our dataset from the Robotcar dataset, and the experimental results suggest that our planner is able to reliably generate trajectories in various driving tasks, such as turning at different intersections, lane-keeping on curved roads and changing lanes for collision avoidance.
UR - http://www.scopus.com/inward/record.url?scp=85076807633&partnerID=8YFLogxK
U2 - 10.1109/ITSC.2019.8917149
DO - 10.1109/ITSC.2019.8917149
M3 - Conference article published in proceeding or book
AN - SCOPUS:85076807633
T3 - 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019
SP - 2736
EP - 2742
BT - 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019
PB - IEEE
T2 - 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019
Y2 - 27 October 2019 through 30 October 2019
ER -