TY - JOUR
T1 - An Integrated Framework of Decision Making and Motion Planning for Autonomous Vehicles Considering Social Behaviors
AU - Hang, Peng
AU - Lv, Chen
AU - Huang, Chao
AU - Cai, Jiacheng
AU - Hu, Zhongxu
AU - Xing, Yang
N1 - Funding Information:
Manuscript received April 7, 2020; revised September 5, 2020 and November 9, 2020; accepted November 22, 2020. Date of publication November 25, 2020; date of current version January 22, 2021. This work was supported in part by the SUG-NAP under Grant M4082268.050 of Nanyang Technological University, Singapore and in part by A∗STAR under Grant 1922500046, Singapore. The review of this article was coordinated by Dr. E. Velenis. (Corresponding author: Chen Lv.) The authors are with the School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore (e-mail: [email protected]; [email protected]; [email protected]; [email protected]; [email protected]; [email protected]). Digital Object Identifier 10.1109/TVT.2020.3040398
Publisher Copyright:
© 2021 IEEE.
PY - 2020/12
Y1 - 2020/12
N2 - This paper presents a novel integrated approach to deal with the decision making and motion planning for lane-change maneuvers of autonomous vehicle (AV) considering social behaviors of surrounding traffic occupants. Reflected by driving styles and intentions of surrounding vehicles, the social behaviors are taken into consideration during the modelling process. Then, the Stackelberg Game theory is applied to solve the decision-making, which is formulated as a non-cooperative game problem. Besides, potential field is adopted in the motion planning model, which uses different potential functions to describe surrounding vehicles with different behaviors and road constraints. Then, Model Predictive Control (MPC) is utilized to predict the state and trajectory of the autonomous vehicle. Finally, the decision-making and motion planning is then integrated into a constrained multi-objective optimization problem. Three testing scenarios considering different social behaviors of surrounding vehicles are carried out to validate the performance of the proposed approach. Testing results show that the integrated approach is able to address different social interactions with other traffic participants, and make proper and safe decisions and planning for autonomous vehicles, demonstrating its feasibility and effectiveness.
AB - This paper presents a novel integrated approach to deal with the decision making and motion planning for lane-change maneuvers of autonomous vehicle (AV) considering social behaviors of surrounding traffic occupants. Reflected by driving styles and intentions of surrounding vehicles, the social behaviors are taken into consideration during the modelling process. Then, the Stackelberg Game theory is applied to solve the decision-making, which is formulated as a non-cooperative game problem. Besides, potential field is adopted in the motion planning model, which uses different potential functions to describe surrounding vehicles with different behaviors and road constraints. Then, Model Predictive Control (MPC) is utilized to predict the state and trajectory of the autonomous vehicle. Finally, the decision-making and motion planning is then integrated into a constrained multi-objective optimization problem. Three testing scenarios considering different social behaviors of surrounding vehicles are carried out to validate the performance of the proposed approach. Testing results show that the integrated approach is able to address different social interactions with other traffic participants, and make proper and safe decisions and planning for autonomous vehicles, demonstrating its feasibility and effectiveness.
KW - autonomous vehicle
KW - Decision-making
KW - game theory
KW - motion planning
KW - potential field
KW - social behaviors
UR - http://www.scopus.com/inward/record.url?scp=85097133701&partnerID=8YFLogxK
U2 - 10.1109/TVT.2020.3040398
DO - 10.1109/TVT.2020.3040398
M3 - Journal article
AN - SCOPUS:85097133701
SN - 0018-9545
VL - 69
SP - 14458
EP - 14469
JO - IEEE Transactions on Vehicular Technology
JF - IEEE Transactions on Vehicular Technology
IS - 12
M1 - 9271859
ER -