Path Tracking Lateral Control of Self-driving Vehicles Based on the Optimal State Point
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摘要: 对于小区、施工导流路段等狭窄区域，很难保证大中型车辆安全地通过.针对这类情况，本文研究了整个车体的路径跟踪横向控制问题，提出了车辆最优状态点和最优参考状态的概念.为了求解最优状态点，本文构造了车辆参考状态所对应的整体偏差这一目标函数，基于车辆运动学模型，依据最优状态点处车辆与参考轨迹的偏差设计了横向控制器，并利用Lyapunov方法证明了该控制系统的稳定性.与车体特定位置的横向偏差相比，整体偏差更为显著地反映了整个车体的跟踪性能.最后，在具有代表性的狭窄区域场景和普通城区道路场景分别进行了仿真实验，结果表明该方法能够有效提高车辆低速行驶时的整体跟踪精度，不仅可以保证车辆安全裕度较大地通过狭窄区域，而且也提升了车辆在城区交通场景驾驶的安全性.Abstract: It is difficult for medium and large vehicles to pass through the narrow area, such as the road of the residential area and the construction diversion. In this paper, the path tracking lateral control problem of the whole vehicle for the above mentioned case is investigated. The optimal state point and the reference state of the vehicle are proposed. In order to select this point, this paper constructs the overall deviation function of the vehicle reference state. In addition, based on the kinematic model, a lateral controller is designed by the deviation from the vehicle to the reference path at this state point. The Lyapunov method is applied to prove the stability of the control system. Compared to the lateral deviation from a particular position, the overall deviation reflects the tracking performance of the whole vehicle more significantly. Finally, simulation results in the typical narrow area and on the urban road are presented to illustrate the higher overall precision of the proposed method for path tracking at low speed. It not only can ensure the vehicle to pass the narrow area with large margin, but also can improve driving safety on the urban road.
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Key words:
- Narrow area /
- overall deviation /
- optimal state point /
- optimal reference state /
- lateral control
1) 本文责任编委 魏庆来 -
表 1 车辆参数
Table 1 Vehicle parameters
参数 数值 单位 前轮胎侧偏刚度$(C_f)$ 80 000 ${\rm N/rad}$ 后轮胎侧偏刚度$(C_r)$ 80 000 ${\rm N/rad}$ 车辆质量$(m)$ 1 960 ${\rm kg}$ 偏航转动惯量$(I_z)$ 3 580 ${\rm kg\cdot m^2}$ 重心与前轴距离$(l_f)$ 1.300 ${\rm m}$ 重心与后轴距离$(l_r)$ 1.788 ${\rm m}$ 最大转角$(\delta_{\max})$ $\dfrac{\pi}{6}$ ${\rm rad}$ 
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表 2 左转弯场景偏差对比
Table 2 A comparison of the deviation on the left corner
控制方法 车体平均偏差$({\rm m})$ 车体最大偏差$({\rm m})$ 模型预测控制 0.261 0.509 后轮控制 0.234 0.646 前轮控制 0.411 0.673 预瞄PID法 0.519 0.752 纯跟踪法 0.471 0.729 本文方法 0.196 0.418
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表 3 右转弯场景偏差对比
Table 3 A comparison of the deviation on the right corner
控制方法 车体平均偏差$({\rm m})$ 车体最大偏差$({\rm m})$ 模型预测控制 0.456 0.847 后轮控制 0.461 1.217 前轮控制 0.748 1.207 预瞄PID法 0.839 1.252 纯跟踪法 0.952 1.358 本文方法 0.361 0.673
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表 4 小曲率弯道场景偏差对比
Table 4 A comparison of the deviation on the small curvature road
控制方法 车体平均偏差$({\rm m})$ 车体最大偏差$({\rm m})$ 模型预测控制 0.012 0.025 后轮控制 0.012 0.033 前轮控制 0.019 0.033 预瞄PID法 0.033 0.045 纯跟踪法 0.043 0.057 本文方法 0.010 0.020
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表 5 直角弯道场景偏差对比
Table 5 A comparison of the deviation on the right angle road
控制方法 车体平均偏差$({\rm m})$ 车体最大偏差$({\rm m})$ 模型预测控制 0.185 0.373 后轮控制 0.213 0.567 前轮控制 0.289 0.477 预瞄PID法 0.624 0.836 纯跟踪法 0.574 0.786 本文方法 0.166 0.326
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表 6 "S"型弯道场景偏差对比
Table 6 A comparison of the deviation on the "S" curved road
控制方法 车体平均偏差$({\rm m})$ 车体最大偏差$({\rm m})$ 模型预测控制 0.117 0.236 后轮控制 0.118 0.322 前轮控制 0.183 0.317 预瞄PID法 0.286 0.419 纯跟踪法 0.353 0.492 本文方法 0.103 0.200
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