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

Article
  • 46 Downloads

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.

Keywords

Intelligent computing budget allocation Trajectory planning On-road planning Intelligent vehicles Ordinal optimization 

CLC number

TP242.6 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 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/ie202224wCrossRefGoogle Scholar
  2. 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
  3. 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.2014.2336271CrossRefGoogle Scholar
  4. 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.1070.0721CrossRefMATHGoogle Scholar
  5. Chen, C., Lee, L.H., 2010. Stochastic Simulation Optimization: an Optimal Computing Budget Allocation. World Scientific, USA.CrossRefGoogle Scholar
  6. 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.2005.007113CrossRefGoogle Scholar
  7. 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:1008349927281MathSciNetCrossRefMATHGoogle Scholar
  8. 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
  9. 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.49.5.732.10615CrossRefGoogle Scholar
  10. 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.2012.2198214CrossRefGoogle Scholar
  11. 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
  12. 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.2006.888594CrossRefGoogle Scholar
  13. 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.2010.2046037CrossRefGoogle Scholar
  14. 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
  15. Ho, Y., Zhao, Q., Jia, Q., 2007. Ordinal Optimization: Soft Optimization for Hard Problems. Springer, New York, USA. http://dx.doi.org/10.1007/978-0-387-68692-9CrossRefMATHGoogle Scholar
  16. 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/10.1145/502109.502111CrossRefGoogle Scholar
  17. 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
  18. 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.2008.2012116CrossRefGoogle Scholar
  19. 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.2015.2389215CrossRefGoogle Scholar
  20. 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
  21. 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.20258CrossRefGoogle Scholar
  22. 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
  23. 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
  24. 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.20255CrossRefGoogle Scholar
  25. 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

Copyright information

© Journal of Zhejiang University Science Editorial Office and Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Department of AutomationTsinghua UniversityBeijingChina
  2. 2.Department of Automotive EngineeringTsinghua UniversityBeijingChina

Personalised recommendations