基于QP算法的智能车速度规划

Speed Planning of Intelligent Vehicle Based on QP Algorithm

针对结构化道路环境下智能车速度规划问题,提出基于QP(二次规划)算法的速度规划方法。首先根据智能车的当前位置和行驶轨迹建立ST图,描述障碍物和智能车之间的运动状态关系;其次根据QP模型构建速度规划目标函数、设计约束条件并求解;最后参照人工驾驶对于安全性和舒适性的评价指标要求,对速度规划结果进行评价分析。实车实验中,起步时的最大加速度为0.6 m/s^2,制动时最大减速度为-1.25 m/s^2;避让紧急超车车辆时,智能车可在5 s后将车距调整到安全范围内;持续避障时最大加速度为0.4 m/s^2,最大减速度为-0.58 m/s^2,加速度变化率的绝对值最大为0.58 m/s3。实车实验表明该方法的实验结果满足安全性和舒适性要求。

Aiming at the problem of intelligent vehicle speed planning in structured road environment,a speed planning method based on QP(quadratic programming)algorithm is proposed.Firstly,according to the current position and driving trajectory of intelligent vehicle,the ST chart is established to describe the relationship between obstacles and the motion state of the intelligent vehicle.Then,according to QP model,the objective function of speed planning is constructed and the constraints are designed and solved.Finally,the results of speed planning are evaluated and analyzed by referring to the safety and comfort requirements of manual driving.In the real vehicle experiment,the maximum acceleration is 0.6 m/s^2 at start and-1.25 m/s^2 at braking;in avoiding emergency overtaking,the smart car can adjust its distance to a safe range after 5 s;In the continuous obstacle avoidance,the maximum acceleration is 0.4 m/s^2,the maximum deceleration is-0.58 m/s^2,and the absolute value of the acceleration change rate is 0.58 m/s3.The experimental results show that the proposed method can meet the requirements of safety and comfort.