TY - GEN
T1 - A learning model for personalized adaptive cruise control
AU - Chen, Xin
AU - Zhai, Yong
AU - Lu, Chao
AU - Gong, Jianwei
AU - Wang, Gang
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/28
Y1 - 2017/7/28
N2 - This paper develops a learning model for personalized adaptive cruise control that can learn from human demonstration online and mimic a human driver's driving strategies in the dynamic traffic environment. Under the framework of the proposed model, reinforcement learning is used to capture the human-desired driving strategy, and the proportion-integration-differentiation controller is adopted to convert the learning strategy to low-level control commands. The performance of the learning model is tested in the simulation environment built in a driving simulator using PreScan. Experimental results show that the learning model can duplicate human driving strategies with acceptable errors. Moreover, compared with the traditional adaptive cruise control, the proposed model can provide better driving comfort and smoothness in the dynamic situation.
AB - This paper develops a learning model for personalized adaptive cruise control that can learn from human demonstration online and mimic a human driver's driving strategies in the dynamic traffic environment. Under the framework of the proposed model, reinforcement learning is used to capture the human-desired driving strategy, and the proportion-integration-differentiation controller is adopted to convert the learning strategy to low-level control commands. The performance of the learning model is tested in the simulation environment built in a driving simulator using PreScan. Experimental results show that the learning model can duplicate human driving strategies with acceptable errors. Moreover, compared with the traditional adaptive cruise control, the proposed model can provide better driving comfort and smoothness in the dynamic situation.
UR - http://www.scopus.com/inward/record.url?scp=85028054739&partnerID=8YFLogxK
U2 - 10.1109/IVS.2017.7995748
DO - 10.1109/IVS.2017.7995748
M3 - Conference contribution
AN - SCOPUS:85028054739
T3 - IEEE Intelligent Vehicles Symposium, Proceedings
SP - 379
EP - 384
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 -