TY - JOUR
T1 - Personalized Trajectory Planning and Control of Lane-Change Maneuvers for Autonomous Driving
AU - Huang, Chao
AU - Huang, Hailong
AU - Chen, Peng
AU - Gao, Hongbo
AU - Wu, Jingda
AU - Huang, Zhiyu
AU - Lv, Chen
N1 - Funding Information:
Manuscript received September 5, 2020; revised December 15, 2020 and March 1, 2021; accepted April 25, 2021. Date of publication April 29, 2021; date of current version July 8, 2021. This work was supported in part by the SUG-NAP Grant M4082268.050 of Nanyang Technological University, Singapore, and A*STAR Grant 1922500046, Singapore. The review of this article was coordinated by Prof. Moussa Boukhnifer. (Corresponding author: Chen Lv.) Chao Huang, Peng Hang, Jingda Wu, Zhiyu Huang, and Chen Lv 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]).
Publisher Copyright:
© 1967-2012 IEEE.
PY - 2021/6
Y1 - 2021/6
N2 - With the aims of safe, smart and sustainable future mobility, a personalized approach of trajectory planning and control based on user preferences is developed for lane-change of autonomous vehicles in this paper. First, a safe area during the lane change process is identified by using constraint Delaunay triangulation. Then, an improved rapidly-exploring Random Trees (i-RRT) is developed with B-spline to generate the feasible trajectory cluster, which is subject to the safe area boundaries and the vehicle dynamics. To extract a personalized trajectory from this cluster, we firstly adopt the fuzzy linguistic preference relation (FLPR) method to identify users' preferences on driving, which can be reflected by their subjective objectives including driving safety, ride comfort and vehicle stability. Then, the technique for order preference by similarity to ideal situation (TOPSIS) is utilized to solve the multi-objective optimisation problem formulated by considering the user preferences. The algorithms proposed above are integrated, and both simulation and experimental validation are conducted under lane-change scenarios of autonomous driving. Simulation and experiment results show that proposed approach is able to successfully realize personalized trajectory planning and lane-change control, satisfying users' various preferences and simultaneously ensure vehicle safety, demonstrating its feasibility and effectiveness.
AB - With the aims of safe, smart and sustainable future mobility, a personalized approach of trajectory planning and control based on user preferences is developed for lane-change of autonomous vehicles in this paper. First, a safe area during the lane change process is identified by using constraint Delaunay triangulation. Then, an improved rapidly-exploring Random Trees (i-RRT) is developed with B-spline to generate the feasible trajectory cluster, which is subject to the safe area boundaries and the vehicle dynamics. To extract a personalized trajectory from this cluster, we firstly adopt the fuzzy linguistic preference relation (FLPR) method to identify users' preferences on driving, which can be reflected by their subjective objectives including driving safety, ride comfort and vehicle stability. Then, the technique for order preference by similarity to ideal situation (TOPSIS) is utilized to solve the multi-objective optimisation problem formulated by considering the user preferences. The algorithms proposed above are integrated, and both simulation and experimental validation are conducted under lane-change scenarios of autonomous driving. Simulation and experiment results show that proposed approach is able to successfully realize personalized trajectory planning and lane-change control, satisfying users' various preferences and simultaneously ensure vehicle safety, demonstrating its feasibility and effectiveness.
KW - Autonomous driving
KW - lane change
KW - personalized trajectory planning
KW - tracking control
KW - user preference awareness
UR - http://www.scopus.com/inward/record.url?scp=85105105741&partnerID=8YFLogxK
U2 - 10.1109/TVT.2021.3076473
DO - 10.1109/TVT.2021.3076473
M3 - Journal article
VL - 70
SP - 5511
EP - 5523
JO - IEEE Transactions on Vehicular Technology
JF - IEEE Transactions on Vehicular Technology
IS - 6
M1 - 9419761
ER -