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

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 p...

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Bibliographic Details
Published in:信息与电子工程前沿：英文版 2016, Vol.17 (6), p.553-565
Main Author: Xiao-xin FU Yong-heng JIANG De-xian HUANG Jing-chun WANG Kai-sheng HUANG
Format: Article
Language:English
Subjects:
分配
智能计算
规划算法
轨迹曲线
轨迹规划
迭代次数
道路
预算
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Summary: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.
ISSN:2095-9184
2095-9230