Personalized Trajectory Planning and Control of Lane-Change Maneuvers for Autonomous Driving

• Published in: IEEE transactions on vehicular technology Vol. 70; no. 6; pp. 5511 - 5523
• Main Authors: Huang, Chao, Huang, Hailong, Hang, Peng, Gao, Hongbo, Wu, Jingda, Huang, Zhiyu, Lv, Chen
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Autonomous driving; Autonomous vehicles; Clusters; Customization; Delaunay triangulation; Feasibility; Identification methods; lane change; Lane changing; Linguistics; Maneuvers; Multiple objective analysis; Optimization; Passenger comfort; personalized trajectory planning; Planning; Preferences; tracking control; Trajectory; Trajectory control; Trajectory planning; Trajectory tracking; user preference awareness; User satisfaction; Vehicle dynamics; Vehicle safety
• ISSN: 0018-9545; 1939-9359
• DOI: 10.1109/TVT.2021.3076473

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.