TY - GEN
T1 - Cooperative Decision Making of Lane-change for Automated Vehicles Considering Human-like Driving Characteristics
AU - Hang, Peng
AU - Lv, Chen
AU - Huang, Chao
AU - Hu, Zhongxu
N1 - Funding Information:
This work was supported in part by the SUG-NAP Grant (No. M4082268.050) of Nanyang Technological University, Singapore, and A*STAR Grant (No. 1922500046), Singapore. * Corresponding author
Publisher Copyright:
© 2021 Technical Committee on Control Theory, Chinese Association of Automation.
PY - 2021/7/26
Y1 - 2021/7/26
N2 - To deal with the cooperative lane-change decision-making issue of autonomous vehicles (AVs), a cooperative coalitional game approach is utilized considering human-like driving characteristics. Firstly, three different driving characteristics are defined for AVs, i.e., aggressive, moderative and conservative. Then, the single-track vehicle model is applied to the system modeling for lane-change decision making. Moreover, the cost function of decision making is constructed considering three vital performance indexes, i.e., safety, comfort and efficiency. Furthermore, the cooperative lane-change decision-making issue is transformed into an optimization problem with multi-constraints via the cooperative coalitional game approach. Finally, testing cases are carried out to verify the feasibility and effectiveness of the proposed approach. Testing results indicate that the designed algorithm is able to make safe and correct lane-change decisions for AVs. Additionally, it can adapt to different driving characteristics of AVs.
AB - To deal with the cooperative lane-change decision-making issue of autonomous vehicles (AVs), a cooperative coalitional game approach is utilized considering human-like driving characteristics. Firstly, three different driving characteristics are defined for AVs, i.e., aggressive, moderative and conservative. Then, the single-track vehicle model is applied to the system modeling for lane-change decision making. Moreover, the cost function of decision making is constructed considering three vital performance indexes, i.e., safety, comfort and efficiency. Furthermore, the cooperative lane-change decision-making issue is transformed into an optimization problem with multi-constraints via the cooperative coalitional game approach. Finally, testing cases are carried out to verify the feasibility and effectiveness of the proposed approach. Testing results indicate that the designed algorithm is able to make safe and correct lane-change decisions for AVs. Additionally, it can adapt to different driving characteristics of AVs.
KW - autonomous vehicle
KW - Decision making
KW - driving characteristic
KW - game theory
KW - human-like
KW - lane change
UR - http://www.scopus.com/inward/record.url?scp=85117307088&partnerID=8YFLogxK
U2 - 10.23919/CCC52363.2021.9550305
DO - 10.23919/CCC52363.2021.9550305
M3 - Conference article published in proceeding or book
AN - SCOPUS:85117307088
T3 - Chinese Control Conference, CCC
SP - 6106
EP - 6111
BT - Proceedings of the 40th Chinese Control Conference, CCC 2021
A2 - Peng, Chen
A2 - Sun, Jian
PB - IEEE Computer Society
T2 - 40th Chinese Control Conference, CCC 2021
Y2 - 26 July 2021 through 28 July 2021
ER -