基于自主漂移的自动驾驶车辆极限工况轨迹规划与控制

doi:10.19562/j.chinasae.qcgc.2024.10.006

Abstract

Abstract:

To consider both stability and trajectory tracking performance of autonomous vehicles operating in extreme conditions， a trajectory planning and control method based on autonomous drift is proposed. A neural network tire dynamics model is designed based on neural network to improve the accuracy of the traditional magic tire formulation. In order to further expand the stability boundaries under the extreme working conditions of autonomous vehicles， the drift stability boundaries are designed based on the tire saturation and maximum sideslip characteristics combined with the center-of-mass lateral deflection angle-transverse swing angular velocity phase plane constraints during drift， and the nonlinear model predictive control （NMPC） is used to plan a safe drift trajectory within a wider stability range， and the drift tracking control is carried out for the planned trajectory. The results of the joint simulation of Simulink/CarSim show that the method can fully utilize the advantages of drift motion to ensure that the vehicle does not go out of control under extreme working conditions， while accurately tracking the desired trajectory.

Key words: extreme conditions, trajectory tracking, stability control, neural network tire model, nonlinear model predictive control

Shaobo Lu,Lingfeng Dai,Chenhui Wang,Bingjun Liu,Zhigang Chu,Wenke Xie. Trajectory Planning and Control of Autonomous Vehicle Under Extreme Conditions Based on Autonomous Drift[J].Automotive Engineering, 2024, 46(10): 1780-1789.

Figures/Tables 13

References 25

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参数	数值
车辆质量m/kg	1 501
质心距前轴距离a/m	1.12
质心距后轴距离b/m	1.50
转动惯量I_z /（kg·m²）	1 816
前轮侧偏刚度C_α_F/（N·rad^-1）	141 643
后轮侧偏刚度C_α_R/（N·rad^-1）	112 441
预测步长N_p	12
控制步长N_c	8
规划时间步长T_p/s	0.01
控制时间步长T_c/s	0.01

轮胎模型	最大误差/N	稳态误差/N
刷子轮胎公式	2 916.15	799.12
魔术轮胎公式	1 946.25	319.22
NN轮胎模型	948.53	111.30

误差	保守稳定边界	漂移稳定边界
误差平均值/m	1.41	0.44
误差最大值/m	5.62	1.38

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Trajectory Planning and Control of Autonomous Vehicle Under Extreme Conditions Based on Autonomous Drift

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