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主題詞:自動駕駛車輛 避撞控制 模型預測控制 勢場函數
中圖分類號:U471.15" "文獻標志碼:A" "DOI: 10.19620/j.cnki.1000-3703.20240708
Collision Avoidance Trajectory Planning and Predictive Tracking Control for Vehicles Based on Adaptive Potential Field
He Qing1, Xu Yueyun2,3, Bian Yougang2,4, Qin Hongmao2,4, Han Zirui2
(1. Shanghai Jike Blue New Energy Technology Co., Ltd., Shanghai 200000; 2. State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle, Hunan University, Changsha 410082; 3. Guoqi (Beijing) Intelligent Connected Vehicle Research Institute Co., Ltd., Beijing 100176; 4. Wuxi Intelligent Control Research Institute of Hunan University, Wuxi 214115)
【Abstract】In order to ensure the safety and lateral stability of the vehicles during the collision avoidance process, this paper proposes a collision avoidance trajectory planning and predictive tracking control method for vehicles based on adaptive potential field. Firstly, the vehicle point mass model is established, the adaptive potential field function is designed and the nonlinear model predictive control problem is constructed to solve the locally optimal trajectory. Secondly, the vehicle lateral stability performance is analyzed, the phase plane constraint and indirect constraint are designed, and the trajectory tracking controller is designed based on the model predictive control method to realize the locally optimal trajectory tracking. The joint simulation of CarSim and Simulink verifies that the proposed method can improve the lateral stability performance of the vehicle while avoiding collision. At the meanwhile, the real-time and effectiveness of the method are further proved by real vehicle tests.
Key words: Autonomous vehicles, Collision avoidance control, Model predictive control, Potential field function
【引用格式】 何慶, 徐月云, 邊有鋼, 等. 基于自適應勢場的車輛避撞軌跡規劃與跟蹤控制[J]. 汽車技術, 2024(10): 8-19.
HE Q, XU Y Y, BIAN Y G, et al. Collision Avoidance Trajectory Planning and Predictive Tracking Control for Vehicles Based on Adaptive Potential Field[J]. Automobile Technology, 2024(10): 8-19.
1 前言
自動駕駛車輛通過實時感知、分析周圍環境,準確判斷道路狀況,從而進行智能決策和操作[1],提升了道路交通安全,降低了交通事故的風險。合理規劃車輛避障軌跡及跟蹤控制,實現車輛避撞,是當前亟待解決的關鍵性問題。
對于局部軌跡規劃,傳統Dijkstra和A*搜索算法雖能生成無碰撞路徑,但未考慮車輛動力學約束,導致軌跡不連續[2-3]。基于采樣的方法可應對復雜環境,但在高動態場景下效果有限[4]。樣條曲線[5]、貝塞爾曲線[6-7]等方法通過插值生成平滑軌跡,但難以滿足運動約束。基于數值優化的模型預測控制(Model Predictive Control, MPC)能夠處理多約束和多目標問題,逐漸成為應對復雜軌跡規劃的主流方法[8-9]。……