Skip to main content

A Multi-objective Optimization Model for Determining the Optimal Standard Feasible Neighborhood of Intelligent Vehicles

  • Conference paper
  • First Online:
PRICAI 2018: Trends in Artificial Intelligence (PRICAI 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11012))

Included in the following conference series:

Abstract

Due to the complex conditions on the roads, intelligent vehicles need to face infinite environments. Based on the neighborhood control theory, the infinite world can be reduced to a limited and irregular feasible area. By constructing a finite number of feasible neighborhoods within the feasible region, an optimal standard feasible neighborhood can be determined for intelligent vehicle. This paper proposes a standard feasible neighborhood for the intelligent vehicle based on the ladder-sector. Then a new multi-objective optimization model for determining the optimal standard feasible neighborhood has been proposed. A partition method has been designed by transforming the problem from the infinite feasible domain into a series of feasible neighborhoods. Finally, simulations have been carried out on some representative road conditions. Simulation results demonstrate the effectiveness of the proposed multi-objective optimization model.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Bila, C., Sivrikaya, F., Khan, M.A., Albayrak, S.: Vehicles of the future: a survey of research on safety issues. IEEE Trans. Intell. Transp. Syst. 18(5), 1046–1065 (2017)

    Article  Google Scholar 

  2. Zhao, H.: A dynamic optimization decision and control model based on neighborhood systems. In: International Congress on Image and Signal Processing, pp. 1329–1334. IEEE (2014)

    Google Scholar 

  3. Eckart, Z., Thiele, L.: Multi-objective Optimization Using Evolutionary Algorithms. Wiley, Hoboken (2001)

    Google Scholar 

  4. Deb, K., AgRWAal, S., Pratap, A., Meyarivan, T.: A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)

    Article  Google Scholar 

  5. Zhang, Q., Li, H.: MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 11(6), 712–731 (2007)

    Article  Google Scholar 

  6. Xu, Y., Qu, R.: Solving multi-objective multicast routing problems by evolutionary multi-objective simulated annealing algorithms with variable neighbourhoods. J. Oper. Res. Soc. 62(2), 313–325 (2011)

    Article  Google Scholar 

  7. Qu, R., Xu, Y., Castro, J., Landa-Silva, D.: Particle swarm optimization for the Steiner tree in graph and delay-constrained multicast routing problems. J. Heuristics 19(2), 317–342 (2013)

    Article  Google Scholar 

  8. Bailey, T., Nieto, J., Guivant, J., Stevens, M.: Consistency of the EKF-SLAM algorithm. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3562–3568. IEEE (2006)

    Google Scholar 

  9. Corff, S.L., Fort, G., Moulines, E.: Online Expectation Maximization algorithm to solve the SLAM problem. In: Statistical Signal Processing Workshop, pp. 225–228. IEEE (2011)

    Google Scholar 

  10. Lee, H.C., Park, S.K., Choi, J.S., Lee, B.H.: PSO-FastSLAM: an improved FastSLAM framework using particle swarm optimization. In: IEEE International Conference on Systems, Man and Cybernetics, pp. 2763–2768. IEEE Press (2009)

    Google Scholar 

  11. Biswas, R., Limketkai, B., Sanner, S., Thrun, S.: Towards object mapping in non-stationary environments with mobile robots. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1014–1019 (2002)

    Google Scholar 

  12. Wolf, D.F., Sukhatme, G.S.: Towards mapping dynamic environments. In: Proceedings of the International Conference on Advanced Robotics, pp. 594–600 (2003)

    Google Scholar 

  13. Wang, C.C., Thorpe, C.: Simultaneous localization and mapping with detection and tracking of moving objects. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 2918–2924. IEEE (2002)

    Google Scholar 

  14. Bhattacharya, S., Ghrist, R., Kumar, V.: Persistent homology for path planning in uncertain environments. IEEE Trans. Robot. 31(3), 578–590 (2017)

    Article  Google Scholar 

  15. Zhong, C., Liu, S., Yang, F.: Topological map building based on GNG network dynamic environment. J. East China Univ. Sci. Technol. (Nat. Sci. Ed.) 38(1), 63–68 (2012)

    Google Scholar 

  16. Sun, L., Shi, Q.: Neighborhood based decision making model for microscopic pedestrians simulation. Highw. Eng. 04, 68–72 (2009)

    Google Scholar 

  17. Xiong, S., Zhao, H.: Simulations on the movement of intelligent vehicle based on rectangular safe neighborhood. Appl. Res. Comput. 30(12), 3593–3596 + 3621 (2013)

    Google Scholar 

  18. Zhao, H.: Motion planning for intelligent cars following roads based on feasible neighborhood. In: IEEE International Conference on Control Science and Systems Engineering, pp. 27–31. IEEE (2015)

    Google Scholar 

  19. Fu, H., Zhao, H., Jiang, Y.: A control method for intelligent car parking based on neighborhood system. Fuzzy Syst. Math. (02), 103–115 (2016)

    Google Scholar 

  20. Jiang, Y., Zhao, H., Fu, H.: A control method to avoid obstacle for an intelligent car based on rough sets and neighborhood systems. In: 10th International Conference on Intelligent Systems and Knowledge Engineering, pp. 66–70. IEEE (2016)

    Google Scholar 

  21. Qian, S., et al.: Operational Research, 3rd edn. Tsinghua University Press, Beijing (2005)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ying Xu .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Huang, L., Xu, Y., Zhao, H. (2018). A Multi-objective Optimization Model for Determining the Optimal Standard Feasible Neighborhood of Intelligent Vehicles. In: Geng, X., Kang, BH. (eds) PRICAI 2018: Trends in Artificial Intelligence. PRICAI 2018. Lecture Notes in Computer Science(), vol 11012. Springer, Cham. https://doi.org/10.1007/978-3-319-97304-3_21

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-97304-3_21

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-97303-6

  • Online ISBN: 978-3-319-97304-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics