Full Text:   <2136>

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CLC number: TP242.6

On-line Access: 2016-06-06

Received: 2015-08-18

Revision Accepted: 2016-01-29

Crosschecked: 2016-05-18

Cited: 0

Clicked: 5865

Citations:  Bibtex RefMan EndNote GB/T7714


Yong-heng Jiang


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Frontiers of Information Technology & Electronic Engineering  2016 Vol.17 No.6 P.553-565


Intelligent computing budget allocation for on-road trajectory planning based on candidate curves

Author(s):  Xiao-xin Fu, Yong-heng Jiang, De-xian Huang, Jing-chun Wang, Kai-sheng Huang

Affiliation(s):  Department of Automation, Tsinghua University, Beijing 100084, China; more

Corresponding email(s):   fuxx10@mails.tsinghua.edu.cn, jiangyh@tsinghua.edu.cn

Key Words:  Intelligent computing budget allocation, Trajectory planning, On-road planning, Intelligent vehicles, Ordinal optimization

Xiao-xin Fu, Yong-heng Jiang, De-xian Huang, Jing-chun Wang, Kai-sheng Huang. 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.

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publisher="Zhejiang University Press & Springer",

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A1 - Xiao-xin Fu
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A1 - De-xian Huang
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A1 - Kai-sheng Huang
J0 - Frontiers of Information Technology & Electronic Engineering
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.1500269

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.

This is a nice paper presenting useful and interesting algorithms and applications. The application is novel and highly interesting.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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