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清华大学学报(自然科学版)  2016, Vol. 56 Issue (3): 273-280    DOI: 10.16511/j.cnki.qhdxxb.2016.21.024
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基于最优计算量分配的公路轨迹规划
付骁鑫1, 江永亨1, 黄德先1, 王京春1, 黄开胜2
1. 清华大学自动化系, 北京 100084;
2. 清华大学汽车工程系, 北京 100084
On-road trajectory planning based on optimal computing budget allocation
FU Xiaoxin1, JIANG Yongheng1, HUANG Dexian1, WANG Jingchun1, HUANG Kaisheng2
1. Department of Automation, Tsinghua University, Beijing 100084, China;
2. Department of Automotive Engineering, Tsinghua University, Beijing 100084, China
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摘要 针对智能汽车的公路轨迹规划问题, 本文将最优计算量分配(OCBA)的思想引入基于候选轨迹曲线的规划算法OODE, 提出新算法OCBA_OODE。OODE通过比较各候选曲线的"粗糙"(存在偏差但计算量小)评价确定最优轨迹曲线。曲线评价随着投入计算量的增加逐渐收敛至准确值, OODE对各曲线平均分配计算量, OCBA_OODE基于曲线评价循环分配计算量进而提高算法效率。OCBA_OODE在求解质量不下降的前提下, 规划速度比OODE的快20%。
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付骁鑫
江永亨
黄德先
王京春
黄开胜
关键词 最优计算量分配轨迹规划智能汽车序优化    
Abstract:This paper presents an algorithm named OCBA_OODE for on-road trajectory planning by using optimal computing budget allocation (OCBA) in a candidate-curve-based planning algorithm named OODE. OODE picks the best trajectory by comparing rough (biased but computationally inexpensive) evaluations of a set of candidate curves. The curve evaluation converges to the real value as the computing budget increases. OODE allocates the equal parts of the computing budget to each curve, while OCBA_OODE repeatedly allocates the budget according to the latest curve evaluations to improve the planning efficiency. OCBA_OODE is 20% faster than OODE while maintaining the same solution quality.
Key wordsoptimal computing budget allocation    trajectory planning    intelligent vehicles    ordinal optimization
收稿日期: 2015-09-22      出版日期: 2016-04-01
ZTFLH:  TP242.6  
通讯作者: 江永亨,副教授,E-mail:jiangyh@tsinghua.edu.cn     E-mail: jiangyh@tsinghua.edu.cn
引用本文:   
付骁鑫, 江永亨, 黄德先, 王京春, 黄开胜. 基于最优计算量分配的公路轨迹规划[J]. 清华大学学报(自然科学版), 2016, 56(3): 273-280.
FU Xiaoxin, JIANG Yongheng, HUANG Dexian, WANG Jingchun, HUANG Kaisheng. On-road trajectory planning based on optimal computing budget allocation. Journal of Tsinghua University(Science and Technology), 2016, 56(3): 273-280.
链接本文:  
http://jst.tsinghuajournals.com/CN/10.16511/j.cnki.qhdxxb.2016.21.024  或          http://jst.tsinghuajournals.com/CN/Y2016/V56/I3/273
  图1 车身表示
  表1 路线代价和行驶代价的计算
  图2 OODE算法的工作框架
  表2 TRP在不同1和ξ 下的统计结果
  图3 基于OCBA 确定“最优”轨迹曲线
  图4 μe(I)和σe(I) 与I 的关系
  图55 一种典型交通场景
  表3 交通场景S1、S2、S3、S44和SR 的场景参数
  图6 OODE和OCBA_OODE的TRP
  表4 DE、OODE和OCBA_OODE的算法性能比较
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