吉林大学学报(工学版) ›› 2025, Vol. 55 ›› Issue (8): 2802-2816.doi: 10.13229/j.cnki.jdxbgxb.20231236

• 通信与控制工程 • 上一篇    

基于自适应采样的智能车辆轨迹规划方法

赵俊武1(),曲婷1,2,胡云峰1,3()   

  1. 1.吉林大学 汽车底盘集成与仿生全国重点实验室，长春 130025
    2.吉林大学 重庆研究院，重庆 401120
    3.吉林大学 通信工程学院，长春 130012
  • 收稿日期:2023-11-11 出版日期:2025-08-01 发布日期:2025-11-14
  • 通讯作者: 胡云峰 E-mail:zhaojw19@mails.jlu.edu.cn;huyf@jlu.edu.cn
  • 作者简介:赵俊武（1994-），男，博士研究生. 研究方向：智能车辆决策规划. E-mail：zhaojw19@mails.jlu.edu.cn
  • 基金资助:
    吉林省科技厅重点研发项目(20230201109GX);重庆市自然科学基金面上项目(CSTB2023NSCQ-MSX1019)

Trajectory planning for intelligent vehicles based on adaptive sampling

Jun-wu ZHAO1(),Ting QU1,2,Yun-feng HU1,3()   

  1. 1.National Key Laboratory of Automotive Chassis Integration and Bionics，Jilin University，Changchun 130025，China
    2.Chongqing Research Institute，Jilin University，Chongqing 401120，China
    3.College of Communication Engineering，Jilin University，Changchun 130012，China
  • Received:2023-11-11 Online:2025-08-01 Published:2025-11-14
  • Contact: Yun-feng HU E-mail:zhaojw19@mails.jlu.edu.cn;huyf@jlu.edu.cn

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摘要：

针对在三维空间（X-Y-T）进行智能车辆轨迹规划时计算复杂度高、耗时长的问题，本文提出了一种基于自适应采样的双层三阶段轨迹规划方法，基于Frenet坐标系把三维轨迹规划问题分解为路径规划和速度规划2个二维优化问题。首先，在路径规划方面，引入基于人工势场的自适应采样方法，以减少路径规划空间采样点数量，采用动态规划计算连接各采样点综合代价最低的路径曲线，构建二次规划问题进一步优化此路径，得到最终规划路径。其次，在速度规划方面，根据运动学约束确定速度规划空间自适应采样区域，使用动态规划方法计算连接各采样点综合代价最低的速度曲线，并依据此曲线构建二次规划问题，求解得到最终速度规划结果。最后，构建轨迹跟踪误差模型，设计横、纵向控制器对规划轨迹可跟踪性进行验证。仿真结果表明，本文所提方法能够将单次规划耗时降低至0.1 s左右，为智能车辆提供了更新频率为10 Hz的规划轨迹，实现了规划与控制的协同。

关键词: 车辆工程, 智能车辆, 轨迹规划, 自适应采样

Abstract:

To address the issue of high computational complexity and excessive time consumption of intelligent vehicle trajectory planning in three-dimensional space （X-Y-T）， this paper proposes a dual-layer， three-stage trajectory planning method based on adaptive sampling. By using the Frenet coordinate system， the three-dimensional trajectory planning problem is decomposed into two two-dimensional optimization problems： path planning and speed planning. Firstly， in terms of path planning， an adaptive sampling method based on artificial potential fields is introduced to reduce the number of path planning space sampling points. Dynamic programming is used to calculate the path curve that connects each sampling point with the lowest comprehensive cost， and a quadratic programming problem is constructed to further optimize this path to obtain the final planned path. Secondly，in terms of speed planning， the adaptive sampling area for speed planning space is determined according to kinematic constraints. The dynamic programming method is used to calculate the speed curve that connects each sampling point with the lowest comprehensive cost， and a quadratic programming problem is constructed based on this curve to obtain the final speed planning result. Finally， a trajectory tracking error model is constructed， and lateral and longitudinal controllers are designed to verify the trackability of the planned trajectory. Simulation results show that the proposed method in this paper can reduce the planning time to about 0.1 seconds per planning cycle， providing a planned trajectory with a frequency of 10 Hz for intelligent vehicles， achieving coordination between planning and control.

Key words: vehicle engineering, intelligent vehicles, trajectory planning, adaptive sampling

中图分类号: 

  • U270.1

图 1

分层轨迹规划系统整体框架"

图 2

Frenet坐标系和笛卡尔坐标系下对车道中心线的描述"

图 3

Frenet坐标系下规划轨迹和参考轨迹"

图 4

连接各列采样点的五次多项式曲线"

图 5

车辆几何轮廓示意图"

图 6

动态障碍物在ST图中投影后构成的可行区域"

图 7

Frenet坐标系下行驶轨迹和规划轨迹"

图8

纵向位置和速度跟踪控制器"

表1

PID控制器参数设置表"

PID控制器比例系数KP积分系数KI微分系数KD
PID10.50.30.1
PID21.80.50.1

表2

车辆参数表"

参 数数值
整车质量/kg1 820
车辆质心至前轴距离/m1.170
车辆质心至后轴距离/m1.770
轮距/m1.620
前轮侧偏刚度/（N·rad-1）52 150
后轮侧偏刚度/（N·rad-1）41 400
绕x轴转动惯量/（kg·m2）1 023.8
绕y轴转动惯量/（kg·m2）3 567.2
绕z轴转动惯量/（kg·m2）4 095.0

图9

多静态障碍物驾驶场景"

图10

车辆和静态障碍物轨迹时空图"

图11

规划路径、速度、航向角及其跟踪误差曲线"

图12

规划路径曲率和横摆角速度曲线"

图13

多动态障碍物驾驶场景"

图14

车辆和动态障碍物轨迹时空图"

图15

规划路径、速度、航向角及其跟踪误差曲线"

图16

规划路径曲率和横摆角速度曲线"

图17

单次规划计算耗时"

图 18

路径规划结果"

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