Seventh-Degree Polynomial-Based Single Lane Change Trajectory Planning and Four-Wheel Steering Model Predictive Tracking Control for Intelligent Vehicles

被引:0
作者
Lai, Fei [1 ,2 ]
Huang, Chaoqun [3 ]
机构
[1] Chongqing Univ Technol, Sch Vehicle Engn, Chongqing 400054, Peoples R China
[2] Minist Educ, Key Lab Adv Mfg Technol Automobile Parts, Chongqing 400054, Peoples R China
[3] Chongqing Technol & Business Inst, Inst Intelligent Mfg & Automot, Chongqing 401520, Peoples R China
基金
中国国家自然科学基金;
关键词
intelligent vehicle; single lane change; trajectory planning; model predictive control; four-wheel steering; 7th-degree polynomial; PATH-TRACKING; AVOIDANCE;
D O I
10.3390/vehicles6040109
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
Single lane changing is one of the typical scenarios in vehicle driving. Planning a suitable single lane changing trajectory and tracking that trajectory accurately is very important for intelligent vehicles. The contribution of this study is twofold: (i) to plan lane change trajectories that cater to different driving styles (including aspects such as safety, efficiency, comfort, and balanced performance) by a 7th-degree polynomial; and (ii) to track the predefined trajectory by model predictive control (MPC) through four-wheel steering. The growing complexity of autonomous driving systems requires precise and comfortable trajectory planning and tracking. While 5th-degree polynomials are commonly used for single-lane change maneuvers, they may fail to adequately address lateral jerk, resulting in less comfortable trajectories. The main challenges are: (i) trajectory planning and (ii) trajectory tracking. Front-wheel steering MPC, although widely used, struggles to accurately track trajectories from point mass models, especially when considering vehicle dynamics, leading to excessive lateral jerk. To address these issues, we propose a novel approach combining: (i) 7th-degree polynomial trajectory planning, which provides better control over lateral jerk for smoother and more comfortable maneuvers, and (ii) four-wheel steering MPC, which offers superior maneuverability and control compared to front-wheel steering, allowing for more precise trajectory tracking. Extensive MATLAB/Simulink simulations demonstrate the effectiveness of our approach, showing improved comfort and tracking performance. Key findings include: (i) improved trajectory tracking: Four-wheel steering MPC outperforms front-wheel steering in accurately following desired trajectories, especially when considering vehicle dynamics. (ii) better ride comfort: 7th-degree polynomial trajectories, with improved control over lateral jerk, result in a smoother driving experience. Combining these two techniques enables safer, more efficient, and more comfortable autonomous driving.
引用
收藏
页码:2228 / 2250
页数:23
相关论文
共 51 条
[1]   A comparison of alternative obstacle avoidance strategies for vehicle control [J].
Alleyne, A .
VEHICLE SYSTEM DYNAMICS, 1997, 27 (5-6) :371-392
[2]   SCR-Normalize: A novel trajectory planning method based on explicit quintic polynomial curves [J].
Analooee, Ali ;
Kazemi, Reza ;
Azadi, Shahram .
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART K-JOURNAL OF MULTI-BODY DYNAMICS, 2020, 234 (04) :650-674
[3]   Nearly time-optimal paths for a ground vehicle [J].
David A. Anisi ;
Johan Hamberg ;
Xiaoming Hu .
Journal of Control Theory and Applications, 2003, 1 (1) :2-8
[4]   Lateral control for autonomous vehicles: A comparative evaluation [J].
Artunedo, Antonio ;
Moreno-Gonzalez, Marcos ;
Villagra, Jorge .
ANNUAL REVIEWS IN CONTROL, 2024, 57
[5]   Model predictive control with fuzzy logic switching for path tracking of autonomous vehicles [J].
Awad, Nada ;
Lasheen, Ahmed ;
Elnaggar, Mahmoud ;
Kamel, Ahmed .
ISA TRANSACTIONS, 2022, 129 :193-205
[6]  
Balint K., 2024, Transp. Res. Proc., V78, P246
[7]   Machine learning for autonomous vehicle's trajectory prediction: A comprehensive survey, challenges, and future research directions [J].
Bharilya, Vibha ;
Kumar, Neetesh .
VEHICULAR COMMUNICATIONS, 2024, 46
[8]   Fixed-structure parameter-dependent state feedback controller: A scaled autonomous vehicle path-tracking application [J].
Borrell, Ariel ;
Puig, Vicenc ;
Sename, Olivier .
CONTROL ENGINEERING PRACTICE, 2024, 147
[9]   Game-Based Lateral and Longitudinal Coupling Control for Autonomous Vehicle Trajectory Tracking [J].
Choi, Young-Min ;
Park, Jahng-Hyon .
IEEE ACCESS, 2022, 10 :31723-31731
[10]   A Two-Stage Real-Time Path Planning: Application to the Overtaking Manuever [J].
Garrido, Fernando ;
Gonzalez, Leonardo ;
Milanes, Vicente ;
Perez Rastelli, Joshue ;
Nashashibi, Fawzi .
IEEE ACCESS, 2020, 8 :128730-128740