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.
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Bai, L., Jiang, Y., Huang, D., 2012. A novel two-level optimization framework based on constrained ordinal optimization and evolutionary algorithms for scheduling of multipipeline crude oil blending. Ind. Eng. Chem. Res., 51(26): 9078–9093. http://dx.doi.org/10.1021/ie202224w
Bechhofer, R.E., Santner, T.J., Goldsman, D.M., 1995. Design and Analysis of Experiments for Statistical Selection, Screening, and Multiple Comparisons. Wiley, New York, USA.
Bengler, K., Dietmayer, K., Farber, B., et al., 2014. Three decades of driver assistance systems: review and future perspectives. IEEE Intell. Transp. Syst. Mag., 6(4): 6–22. http://dx.doi.org/10.1109/MITS.2014.2336271
Branke, J., Chick, S.E., Schmidt, C., 2007. Selecting a selection procedure. Manag. Sci., 53(12): 1916–1932. http://dx.doi.org/10.1287/mnsc.1070.0721
Chen, C., Lee, L.H., 2010. Stochastic Simulation Optimization: an Optimal Computing Budget Allocation. World Scientific, USA.
Chen, C., Yüesan, E., 2005. An alternative simulation budget allocation scheme for efficient simulation. Int. J. Simul. Process Model., 1(1/2):49–57. http://dx.doi.org/10.1504/IJSPM.2005.007113
Chen, C., Lin, J., Yü cesan, E., et al., 2000. Simulation budget allocation for further enhancing the efficiency of ordinal optimization. Discr. Event Dyn. Syst., 10(3): 251–270. http://dx.doi.org/10.1023/A:1008349927281
Chen, C., Chick, S.E., Lee, L.H., et al., 2015. Ranking and selection: efficient simulation budget allocation. In: Fu, M.C. (Ed.), Handbook of Simulation Optimization. Springer, New York, USA. http://dx.doi.org/10.1007/978-1-4939-1384-8_3
Chick, S.E., Inoue, K., 2001. New two-stage and sequential procedures for selecting the best simulated system. Oper. Res., 49(5): 732–743. http://dx.doi.org/10.1287/opre.49.5.732.10615
Chu, K., Lee, M., Sunwoo, M., 2012. Local path planning for off-road autonomous driving with avoidance of static obstacles. IEEE Trans. Intell. Transp. Syst., 13(4): 1599–1616. http://dx.doi.org/10.1109/TITS.2012.2198214
Fu, X., Jiang, Y., Huang, D., et al., 2015. A novel real-time trajectory planning algorithm for intelligent vehicles. Contr. Dec., 30(10): 1751–1758 (in Chinese).
Gehrig, S.K., Stein, F.J., 2007. Collision avoidance for vehicle-following systems. IEEE Trans. Intell. Transp. Syst., 8(2): 233–244. http://dx.doi.org/10.1109/TITS.2006.888594
Glaser, S., Vanholme, B., Mammar, S., et al., 2010. Maneuver-based trajectory planning for highly autonomous vehicles on real road with traffic and driver interaction. IEEE Trans. Intell. Transp. Syst., 11(3): 589–606. http://dx.doi.org/10.1109/TITS.2010.2046037
Hilgert, J., Hirsch, K., Bertram, T., et al., 2003. Emergency path planning for autonomous vehicles using elastic band theory. Proc. IEEE/ASME Int. Conf. on Advanced Intelligent Mechatronics, p.1390–1395. http://dx.doi.org/10.1109/AIM.2003.1225546
Ho, Y., Zhao, Q., Jia, Q., 2007. Ordinal Optimization: Soft Optimization for Hard Problems. Springer, New York, USA. http://dx.doi.org/10.1007/978-0-387-68692-9
Kim, S., Nelson, B.L., 2001. A fully sequential procedure for indifference-zone selection in simulation. ACM Trans. Model. Comput. Simul., 11(3): 251–273. http://dx.doi.org/10.1145/502109.502111
Köhler, S., Schreiner, B., Ronalter, S., et al., 2013. Autonomous evasive maneuvers triggered by infrastructure-based detection of pedestrian intentions. Proc. IEEE Intelligent Vehicles Symp., p.519–526. http://dx.doi.org/10.1109/IVS.2013.6629520
Kuwata, Y., Teo, J., Fiore, G., et al., 2009. Real-time motion planning with applications to autonomous urban driving. IEEE Trans. Contr. Syst. Technol., 17(5): 1105–1118. http://dx.doi.org/10.1109/TCST.2008.2012116
Ma, L., Xue, J., Kawabata, K., et al., 2015. Efficient sampling-based motion planning for on-road autonomous driving. IEEE Trans. Intell. Transp. Syst., 16(4): 1961–1976. http://dx.doi.org/10.1109/TITS.2015.2389215
McNaughton, M., Urmson, C., Dolan, J.M., et al., 2011. Motion planning for autonomous driving with a conformal spatiotemporal lattice. Proc. IEEE Int. Conf. on Robotics and Automation, p.4889–4895. http://dx.doi.org/10.1109/ICRA.2011.5980223
Montemerlo, M., Becker, J., Bhat, S., et al., 2008. Junior: the Stanford entry in the urban challenge. J. Field Robot., 25(9): 569–597. http://dx.doi.org/10.1002/rob.20258
Papadimitriou, I., Tomizuka, M., 2003. Fast lane changing computations using polynomials. Proc. American Control Conf., p.48–53. http://dx.doi.org/10.1109/ACC.2003.1238912
Reif, J.H., 1979. Complexity of the mover’s problem and generalizations. Proc. 20th Annual Symp. on Foundations of Computer Science, p.421–427. http://dx.doi.org/10.1109/SFCS.1979.10
Urmson, C., Anhalt, J., Bagnell, D., et al., 2008. Autonomous driving in urban environments: boss and the urban challenge. J. Field Robot., 25(8): 425–466. http://dx.doi.org/10.1002/rob.20255
Ziegler, J., Stiller, C., 2009. Spatiotemporal state lattices for fast trajectory planning in dynamic on-road driving scenarios. Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, p.1879–1884. http://dx.doi.org/10.1109/IROS.2009.5354448
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Project supported by the National Natural Science Foundation of China (No. 61273039)
ORCID: Yong-heng JIANG, http://orcid.org/0000-0002-9551-9846
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Fu, Xx., Jiang, Yh., Huang, Dx. et al. Intelligent computing budget allocation for on-road trajectory planning based on candidate curves. Frontiers Inf Technol Electronic Eng 17, 553–565 (2016). https://doi.org/10.1631/FITEE.1500269
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DOI: https://doi.org/10.1631/FITEE.1500269
Keywords
- Intelligent computing budget allocation
- Trajectory planning
- On-road planning
- Intelligent vehicles
- Ordinal optimization