Intelligent computing budget allocation for on-road trajectory planning based on candidate curves
- 46 Downloads
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.
KeywordsIntelligent computing budget allocation Trajectory planning On-road planning Intelligent vehicles Ordinal optimization
Unable to display preview. Download preview PDF.
- 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.Google Scholar
- 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_3Google Scholar
- 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).Google Scholar
- 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.2003.1225546Google Scholar
- 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.2013.6629520Google Scholar
- 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.2011.5980223Google Scholar
- 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.2003.1238912Google Scholar
- 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.1979.10Google Scholar
- 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.2009.5354448Google Scholar