TY - GEN
T1 - Human-like lane-change decision making for automated driving with a game theoretic approach
AU - Hang, Peng
AU - Lv, Chen
AU - Huang, Chao
AU - Xing, Yang
AU - Hu, Zhongxu
AU - Cai, Jiacheng
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/12/18
Y1 - 2020/12/18
N2 - With the consideration of personalized driving for automated vehicles (AVs), this paper presents a human-like decision making framework for AVs. In the modelling process, the driver model is combined with the vehicle model, which yields the integrated model for the decision-making algorithm design. Three different driving styles, i.e., aggressive, normal, and conservative, are defined for human-like driving modelling. Additionally, motion prediction algorithm is designed with model predictive control (MPC) to advance the effectiveness of the decision-making approach. Furthermore, the decision-making cost function is constructed considering drive safety, ride comfort and travel efficiency, which reflect different driving styles. Based on the decision-making cost function, a noncooperative game theoretic approach is applied to solving the decision-making issue. Finally, the proposed human-like decision making algorithm is evaluated with an overtaking scenario. Testing results indicate different driving styles cause different decision-making results, and the designed algorithm can always make safe and reasonable decisions for AVs.
AB - With the consideration of personalized driving for automated vehicles (AVs), this paper presents a human-like decision making framework for AVs. In the modelling process, the driver model is combined with the vehicle model, which yields the integrated model for the decision-making algorithm design. Three different driving styles, i.e., aggressive, normal, and conservative, are defined for human-like driving modelling. Additionally, motion prediction algorithm is designed with model predictive control (MPC) to advance the effectiveness of the decision-making approach. Furthermore, the decision-making cost function is constructed considering drive safety, ride comfort and travel efficiency, which reflect different driving styles. Based on the decision-making cost function, a noncooperative game theoretic approach is applied to solving the decision-making issue. Finally, the proposed human-like decision making algorithm is evaluated with an overtaking scenario. Testing results indicate different driving styles cause different decision-making results, and the designed algorithm can always make safe and reasonable decisions for AVs.
KW - Automated vehicle
KW - Decision making
KW - Game theoretic approach
KW - Human-like
KW - Lane change
UR - http://www.scopus.com/inward/record.url?scp=85101122971&partnerID=8YFLogxK
U2 - 10.1109/CVCI51460.2020.9338614
DO - 10.1109/CVCI51460.2020.9338614
M3 - Conference article published in proceeding or book
AN - SCOPUS:85101122971
T3 - 2020 4th CAA International Conference on Vehicular Control and Intelligence, CVCI 2020
SP - 708
EP - 713
BT - 2020 4th CAA International Conference on Vehicular Control and Intelligence, CVCI 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th CAA International Conference on Vehicular Control and Intelligence, CVCI 2020
Y2 - 18 December 2020 through 20 December 2020
ER -