基于制动转向协同控制的智能车紧急避障研究
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摘要
为使车辆在紧急情况下能够快速稳定地完成换道避障,本文中将车辆纵向控制和侧向控制结合在一起,综合考虑车辆在紧急制动转向避障的过程中由于路面附着条件的限制可能会造成车辆失稳问题,在上层进行避障规划过程中加入基于哈密顿能量函数的理想纵向力和侧向力分配,并搭建稳态预测动态校正驾驶员模型跟踪规划的期望路径。然后利用Matlab/Simulink搭建3自由度车辆动力学模型,并基于Carsim和Labview的硬件在环试验对理想纵向力和侧向力的分配进行验证,仿真结果表明,所计算的纵、侧向力分配规律能够在车辆紧急制动转向避障时,在较短的时间和纵向距离条件下行驶到相邻车道。最后通过实车试验进一步验证了所提出方法的有效性。
In order to enable vehicles to change lane quickly and steadily to avoid obstacles in emergencies, this paper combines vehicle longitudinal with lateral control. Considering the stability problems that may be caused by restrictions of road adhesion conditions in an emergency braking steering process of obstacle avoidance. Ideal longitudinal force and lateral force allocation based on Hamilton energy function is added to the planning process of emergency obstacle avoidance, and a steady state prediction with dynamic correction driver model is built to track the desired path of the planning. Then, a three-degree-of-freedom vehicle dynamics model is built by using Matlab/Simulink and the distribution of ideal longitudinal force and lateral force is validated by hardware in-loop test based on Carsim and Labview. The simulation results show that the distribution of the calculated force distribution law can make the vehicle drive to the adjacent lane in a relatively short period of time and longitudinal distance under the condition of emergency braking and steering to avoid obstacles. Finally, a vehicle test is carried out to verify the effectiveness of the proposed method.
In order to enable vehicles to change lane quickly and steadily to avoid obstacles in emergencies, this paper combines vehicle longitudinal with lateral control. Considering the stability problems that may be caused by restrictions of road adhesion conditions in an emergency braking steering process of obstacle avoidance. Ideal longitudinal force and lateral force allocation based on Hamilton energy function is added to the planning process of emergency obstacle avoidance, and a steady state prediction with dynamic correction driver model is built to track the desired path of the planning. Then, a three-degree-of-freedom vehicle dynamics model is built by using Matlab/Simulink and the distribution of ideal longitudinal force and lateral force is validated by hardware in-loop test based on Carsim and Labview. The simulation results show that the distribution of the calculated force distribution law can make the vehicle drive to the adjacent lane in a relatively short period of time and longitudinal distance under the condition of emergency braking and steering to avoid obstacles. Finally, a vehicle test is carried out to verify the effectiveness of the proposed method.
引文
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[18] 高振海.驾驶员最优预瞄加速度模型的研究[D].长春:吉林大学,2000.
[2] 夏如艇,土居俊一.紧急状况下车辆操作特性的仿真研究[J].汽车工程学报,2014,4(1):14-20.
[3] 吴斌,朱西产,沈剑平,等.自然驾驶工况的驾驶员紧急转向变道行为[J].同济大学学报:自然科学版,2017,45(4):554-561.
[4] SINGH A,SINGH P,NISHIHARA O.Obstacle avoidance by steering and braking with minimum total vehicle force[J].Ifac Papersonline,2016,49(19):486-493.
[5] RUDER M,ENKELMANN W,GARNITZ R.Highway lane change assistant[C].Intelligent Vehicle Symposium,2002.IEEE.
[6] ROSOLIA U,BRAGHIN F,ALLEYNE A,et al.NLMPC for real time path following and collision avoidance[C].SAE Paper 2015-01-0313.
[7] 刘英杰,赵又群.基于模型预测控制的汽车换道操纵逆问题求解[J].华南理工大学学报:自然科学版,2013(11):120-124.
[8] 杨建森,郭孔辉,丁海涛,等.基于模型预测控制的汽车底盘集成控制[J].吉林大学学报,2011(s2):1-5.
[9] 李江湖.自动驾驶电动汽车避障控制方法研究[D].南京:东南大学,2015.
[10] JI J,KHAJEPOUR A,MELEK W W,et al.Path planning and tracking for vehicle collision avoidance based on model predictive control with multi-constraints[J].IEEE Transactions on Vehicular Technology,2017,66(2):952-964.
[11] KANARACHOS S.A new min-max methodology for computing optimised obstacle avoidance steering manoeuvres of ground vehicles[J].International Journal of Systems Science,2014,45(5):1042-1057.
[12] CHEN Y,JIANG Y L,GICQUEL P.Vehicle automation-algorithm for changing/merging lane behavior[C].International Conference on Computer,Mechatronics and Electronic Engineering (CMEE 2016)ISBN:978-1-60595-406-6.
[13] KOU I,RAKSINCHAROENSAK P,NAGAI M.A study on shared control between the driver and an active steering control system in emergency obstacle avoidance situations[J].Ifac Proceedings Volumes,2014,47(3):6338-6343.
[14] 张立增.智能汽车方向与速度综合决策的混合机理与规则建模研究[D].长春:吉林大学,2017.
[15] HATTORI Y,ONO E,HOSOE S.An optimum vehicle trajectory control for obstacle avoidance with the shortest longitudinal traveling distance[J].Transactions of the Society of Instrument & Control Engineers,2013,43(11):1047-1054.
[16] 大室朗,服部義和.基于车辆合力最小的避障最优路径控制[J].日本机械学会论文集:c編,2010,76(772):3587-3594.
[17] KANARACHOS S,ALIREZAEI M.An adaptive finite element method for computing emergency manoeuvres of ground vehicles in complex driving scenarios[J].International Journal of Vehicle Systems Modelling & Testing,2015,10(3):239-262.
[18] 高振海.驾驶员最优预瞄加速度模型的研究[D].长春:吉林大学,2000.