Parallel Parking Path Planning and Tracking Control Based on Simulated Annealing Algorithm

To address the issues of curvature discontinuity and terminal tire non-return in the parallel parking of autonomous vehicles, a novel parallel parking path planning method based on the combination of the quintic polynomial curve and an improved sigmoid function was proposed. First, a vehicle kinemat...

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Bibliographic Details
Published inInternational journal of automotive technology Vol. 25; no. 4; pp. 867 - 880
Main Authors Yu, Leiyan, Cai, Yongpeng, Feng, Xiangbo, Zhou, Yuanxue, Hu, Zihua, Tian, Meilan, Sun, Shaohua
Format Journal Article
LanguageEnglish
Published Seoul The Korean Society of Automotive Engineers 01.08.2024
Springer Nature B.V
한국자동차공학회
Subjects
Algorithms
Annealing
Automobile parking
Automotive Engineering
Control algorithms
Control theory
Curvature
Effectiveness
Engineering
Kinematics
Mathematical analysis
Optimal control
Optimization
Parking
Path planning
Path tracking
Polynomials
Predictive control
Simulated annealing
Simulation
Tracking control
Tracking control systems
Vehicle Dynamics and Control
Yaw
자동차공학
Simulated annealing algorithm
Pure pursuit algorithm
Model predictive control algorithm
Parallel parking
Path planning
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Summary:To address the issues of curvature discontinuity and terminal tire non-return in the parallel parking of autonomous vehicles, a novel parallel parking path planning method based on the combination of the quintic polynomial curve and an improved sigmoid function was proposed. First, a vehicle kinematic model was established. Second, considering the position, front wheel angle, and yaw angle constraints during the parking process, a hybrid superimposed curve was designed. The parking path planning problem was converted into an optimal control problem, with the maximum curvature and curvature at both ends as objective functions, and the parameters were optimized using the simulated annealing algorithm. Subsequently, for path tracking control, the simulated annealing algorithm was used to optimize the prediction time horizon of the model predictive control algorithm. Finally, a series of actual vehicle experiments were conducted based on the Apollo Autonomous Driving Developer Suite, and the effectiveness of the proposed path planning method was validated. Therefore, this method can provide a certain reference for automatic parking path planning technology.
ISSN:1229-9138
1976-3832
DOI:10.1007/s12239-024-00087-7