基于分段优化的车辆换道避障轨迹规划

doi:10.19562/j.chinasae.qcgc.2022.06.004

Abstract

Abstract:

In order to enhance the safety and comfort of intelligent vehicle during lane change for collision avoidance under complex road scenes， a trajectory planning method of intelligent vehicle lane change for collision avoidance is proposed based on segmented optimization. Firstly， according to the state of ego vehicle and multiple sampling points， candidate y-x curve clusters and x-t curve clusters are generated based on quintic polynomials with consideration of various possibilities of lane change. Then， by designing an obstacle risk evaluation function based on exponential function， and combined with the smoothness， altruism and driving efficiency of the trajectory， a comprehensive evaluation system is constructed to select the optimal reference trajectory， providing the direction and speed references for intelligent vehicle lane change for obstacle avoidance and preventing the solution from falling into local optimum in trajectory optimization. For suiting to the time-varying features of the obstacle movement state， the piecewise quintic polynomial y-x curve and x-t curve are constructed with the reference trajectory as a guide， and the optimization objective function is also established with consideration of the risk of collision with obstacle vehicles， converting the trajectory optimization problem into a constrained nonlinear programming one to find out the optimal trajectory by exterior point method. Finally， the simulation verification is performed on MATLAB platform with a result showing that the trajectory planning method proposed enhance the environmental adaptability and obstacle avoidance adjustment ability of vehicles， while meeting the requirements of lane-change smoothness and ride comfort.

Key words: intelligent driving, lane change for collision avoidance, trajectory planning, segmented optimization, nonlinear programming

Bin Tang,Zhanxiang Xu,Haobin Jiang,Yingfeng Cai,Zitian Hu,Zhengyi Yang. Trajectory Planning of Intelligent Vehicles in Lane Change for Collision Avoidance Based on Segmented Optimization[J].Automotive Engineering, 2022, 44(6): 831-841.

Figures/Tables 9

初始化
$y = f r e f (x), x = g r e f (t)$ ；
$f o r ? t = t s : t d : t f$
（1）由环境感知系统获取本车与障碍车辆运动状态信息，包括位置和速度；
（2）以 $g r e f (t)$ 作为参考线，构建 $f b e s t (x)$ 的优化目标函数 $J 1$ ；
（3）在约束条件下，求解 $m i n J 1$ 得出 $f b e s t (x)$ 分段曲线系数；
（4）以 $f r e f (x)$ 为参考线，构建 $g b e s t (t)$ 的优化目标函数 $J 2$ ；
（5）在约束条件下，求解 $m i n J 2$ 得出 $g b e s t (t)$ 分段曲线系数；
（6）输出本时刻的最优 $f b e s t (x)$ 和 $g b e s t (t)$ ；
$e n d$

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Trajectory Planning of Intelligent Vehicles in Lane Change for Collision Avoidance Based on Segmented Optimization

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