基于候选曲线的公路轨迹规划中的智能计算量分配

Xiao-xin FU; Yong-heng JIANG; De-xian HUANG; Jing-chun WANG; Kai-sheng HUANG

doi:10.1631/FITEE.1500269

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基于候选曲线的公路轨迹规划中的智能计算量分配

常规文章 | Updated：2022-05-19

- 基于候选曲线的公路轨迹规划中的智能计算量分配
- Intelligent computing budget allocation for on-road trajectory planning based on candidate curves
- 信息与电子工程前沿（英文） 2016年17卷第6期页码：553-565
- Affiliations：
  
  1Department of Automation, Tsinghua University, Beijing 100084, China
  2Department of Automotive Engineering, Tsinghua University, Beijing 100084, China
- Author bio：
  
  E-mail:fuxx10@mails.tsinghua.edu.cn
  E-mail:jiangyh@tsinghua.edu.cn
- Funds：
  
  Project supported by the National Natural Science Foundation of China (No. 61273039)
- DOI：10.1631/FITEE.1500269
  中图分类号： TP242.6
- 收稿：2015-08-18，
  
  录用：2016-1-29，
  
  纸质出版：2016-06
- Accepted：
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基于候选曲线的公路轨迹规划中的智能计算量分配[J]. 信息与电子工程前沿（英文）, 2016,17(6):553-565.

Xiao-xin FU, Yong-heng JIANG, De-xian HUANG, et al. Intelligent computing budget allocation for on-road trajectory planning based on candidate curves[J]. Frontiers of Information Technology & Electronic Engineering, 2016, 17(6): 553-565.
基于候选曲线的公路轨迹规划中的智能计算量分配[J]. 信息与电子工程前沿（英文）, 2016,17(6):553-565. DOI： 10.1631/FITEE.1500269.

Xiao-xin FU, Yong-heng JIANG, De-xian HUANG, et al. Intelligent computing budget allocation for on-road trajectory planning based on candidate curves[J]. Frontiers of Information Technology & Electronic Engineering, 2016, 17(6): 553-565. DOI： 10.1631/FITEE.1500269.

摘要

目的

公路轨迹规划是辅助驾驶和无人驾驶领域中的关键问题。为解决该问题，作者针对传统方法（如势场法、RRT）在求解质量和求解效率方面的不足，提出基于候选曲线的规划算法OODE。OODE分轨迹曲线和加速度变化两部分规划轨迹，采用差分进化（DE）算法通过求解子问题计算各候选曲线的评价，然后通过比较曲线评价从候选者中选取最优曲线。DE的迭代次数越多，曲线评价越准确。本文考虑对不同曲线智能分配迭代计算量，以减少总计算量消耗，同时保证所选最优曲线以足够高的概率是真实最优曲线，从而提高OODE算法的效率。

创新点

提出基于智能计算量分配（ICBA）的轨迹规划算法框架；设计曲线评价预测模型和优质曲线选拔模型，提出基于ICBA的轨迹规划算法IOODE。

方法

基于对优质曲线迭代分配计算量的思想，设计智能计算量分配（ICBA）机制，提出基于ICBA的轨迹规划算法框架（图4）；设计曲线评价预测模型（EPM）和优质曲线选拔模型（CSM），提出基于ICBA的轨迹规划算法IOODE；通过仿真分析IOODE算法的轨迹规划结果（图9、10），验证所提出计算量分配机制的有效性（图12、13）和ICBA对算法效率的提升作用（图14、表5）。

结论

本文中提出的IOODE算法与OODE算法相比，求解质量没有明显区别，但求解速度提升约20%（表5）。

Abstract

In this paper

on-road trajectory planning is solved by introducing intelligent computing budget allocation (ICBA) into a candidate-curve-based planning algorithm

namely

ordinal-optimization-based differential evolution (OODE). The proposed algorithm is named IOODE with ‘I’ representing ICBA. OODE plans the trajectory in two parts: trajectory curve and acceleration profile. The best trajectory curve is picked from a set of candidate curves

where each curve is evaluated by solving a subproblem with the differential evolution (DE) algorithm. The more iterations DE performs

the more accurate the evaluation will become. Thus

we intelligently allocate the iterations to individual curves so as to reduce the total number of iterations performed. Meanwhile

the selected best curve is ensured to be one of the truly top curves with a high enough probability. Simulation results show that IOODE is 20% faster than OODE while maintaining the same performance in terms of solution quality. The computing budget allocation framework presented in this paper can also be used to enhance the efficiency of other candidate-curve-based planning methods.

关键词

Keywords

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