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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| Published in: | 信息与电子工程前沿：英文版 Vol. 17; no. 6; pp. 553 - 565 |
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| Main Author: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2016
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| Subjects: | |
| Online Access: | Get full text |
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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. |
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| Bibliography: | Intelligent computing budget allocation Trajectory planning On-road planning Intelligent vehicles Ordinal optimization 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. 33-1389/TP |
| ISSN: | 2095-9184 2095-9230 |