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

Content Provider	Springer Nature Link
Author	Fu, Xiao xin Jiang, Yong heng Huang, De xian Wang, Jing chun Huang, Kai sheng
Copyright Year	2016
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.
Starting Page	553
Ending Page	565
Page Count	13
File Format	PDF
ISSN	20959184
Journal	Journal of Zhejiang University SCIENCE C
Volume Number	17
Issue Number	6
e-ISSN	20959230
Language	English
Publisher	Zhejiang University Press
Publisher Date	2016-06-11
Publisher Place	Hangzhou
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Intelligent computing budget allocation Trajectory planning On-road planning Intelligent vehicles Ordinal optimization Computer Science Electrical Engineering Computer Hardware Computer Systems Organization and Communication Networks Electronics and Microelectronics, Instrumentation Communications Engineering, Networks
Content Type	Text
Resource Type	Article
Subject	Signal Processing Computer Networks and Communications Electrical and Electronic Engineering Hardware and Architecture

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