文献类型：期刊文献

中文题名：基于三次B样条曲线拟合的智能车轨迹跟踪算法

英文题名：Intelligent vehicle path tracking algorithm based on cubic B-spline curve fitting

作者：张永华[1];杜煜[2];潘峰[2];魏岳[3]

第一作者：张永华

机构：[1]北京联合大学智慧城市学院;[2]北京联合大学机器人学院;[3]保定学院物理与电子工程系

第一机构：北京联合大学智慧城市学院

年份：2018

卷号：38

期号：6

起止页码：1562-1567

中文期刊名：计算机应用

外文期刊名：Journal of Computer Applications

收录：CSTPCD;;北大核心:【北大核心2017】；CSCD:【CSCD_E2017_2018】；

基金：国家自然科学基金资助项目(91420202;61372088)~~

语种：中文

中文关键词：智能车;轨迹跟踪算法;三次B样条曲线拟合;切向角

外文关键词：intelligent vehicle;path tracking algorithm;cubic B-spline curve fitting;tangential angle

摘要：针对传统几何轨迹跟踪算法切向角获取依赖高精度惯导设备的问题,提出了基于三次B样条曲线拟合的轨迹跟踪算法。首先,通过对先验地图中的离散轨迹点进行拟合生成平滑轨迹线;然后,根据轨迹方程通过插值法重新生成离散路点,并计算各个路点处的切向角,从而实现了对多传感器融合轨迹的优化与跟踪。在真实的智能车实验平台上,用所提算法对20 km/h低速绕圈和60 km/h较高速度直道两种典型场景进行了在真实道路下的跟踪测试。在低速大曲率和较高速度直道两种典型场景下,所提算法轨迹跟踪的最大横向误差均保持在0.3 m以内。实验结果表明,该算法有效解决了传统几何轨迹跟踪算法对惯导设备依赖的问题,同时保持了较好的跟踪性能。
The tangential angle acquisition of the traditional geometric path tracking algorithm depends on high precision inertial navigation equipments. In order to solve the problem, a new path tracking algorithm based on cubic B-spline curve fitting was proposed. Firstly, the smooth path was generated by fitting the discrete path points in the priori map. Then, the discrete path points were regenerated by using an interpolation method according to the path equation, and the tangential angle at each point was calculated to realize the optimization and tracking of the multi-sensor fusion path. On the real intelligent vehicle experiment platform, the 20 km/h low-speed-circle and the 60 km/h high-speed-straight-path tracking tests for the proposed algorithm were carried out under the two real road scenes. Under the two typical test scenarios of low-speed-largecurvature and high-speed-straight-path, the maximum lateral error of path tracking of the proposed algorithm is kept within 0. 3 m. The experimental results show that, the proposed algorithm can effectively solve the problem of traditional geometric path tracking algorithm＇s dependence on inertial navigation device, and maintain good tracking performance at the same time.