复杂交通环境无人驾驶车辆防碰撞方法仿真

Simulation of Anti-Collision Method for Unmanned Vehicles in Complex Traffic Environment

无人驾驶车辆自身具有强非线性特征,且信号时延和参数的不确定性特点显著,同时还易受道路附着系数及前后车距离等因素影响。为此提出复杂交通环境无人驾驶车辆防碰撞方法。构建无人驾驶车辆相关的动力学模型,并利用模型预测车辆在行驶过程中的相关数据。采用切比雪夫定律预处理车辆的数据信息,得到前后车辆的运动参数。通过模型预测控制法对参数求解,得到无人驾驶车辆的最佳行驶轨迹与跟踪控制序列,完成复杂交通环境无人驾驶车辆的防碰撞。仿真结果表明,所提方法的路径跟踪效果与给定路径一致、避障能力达到100%,常规状态与紧急状态下的跟踪-避障能力强。因此所提方法具有整体有效性,研究价值较高。

Driverless vehicle has strong nonlinear characteristics,and its signal delay and parameter uncertainty are significant.In addition,it is also susceptible to road adhesion coefficient and the distance between the front and rear vehicles.Therefore,an anti-collision method for driverless vehicles in complex traffic environments was put forward.At first,a dynamics model of the driverless vehicle was constructed.And then,relevant data of driving vehicles was predicted by the model.Moreover,Chebyshev's law was used to preprocess the data of the vehicle and thus to calculate the motion parameters of the front vehicle and the following vehicle.Furthermore,the model predictive control method was adopted to solve the parameters and obtain the optimal trajectory and tracking control sequence of driverless vehicles,thus completing the collision avoidance of driverless vehicles in complex traffic environments.Simulation results prove that the path-tracking effect of the proposed method is consistent with the given path,and the ability of obstacle avoidance reaches 100%.Meanwhile,the tracking-obstacle avoidance ability is strong in normal and emergency states.Consequently,the method has overall validity and high research value.