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Local Path Planning of Driverless Car Navigation Based on Jump Point Search Method Under Urban Environment

Author

Listed:
  • Kaijun Zhou

    (Mobile E-Business Collaborative Innovation Center of Hunan Province, Hunan University of Commerce, Changsha 410205, China
    Key Laboratory of Hunan Province for Mobile Business Intelligence, Hunan University of Commerce, Changsha 410205, China)

  • Lingli Yu

    (School of Information Science and Engineering, Central South University, Changsha 410083, China)

  • Ziwei Long

    (School of Information Science and Engineering, Central South University, Changsha 410083, China)

  • Siyao Mo

    (School of Information Science and Engineering, Central South University, Changsha 410083, China)

Abstract

The Jump Point Search (JPS) algorithm is adopted for local path planning of the driverless car under urban environment, and it is a fast search method applied in path planning. Firstly, a vector Geographic Information System (GIS) map, including Global Positioning System (GPS) position, direction, and lane information, is built for global path planning. Secondly, the GIS map database is utilized in global path planning for the driverless car. Then, the JPS algorithm is adopted to avoid the front obstacle, and to find an optimal local path for the driverless car in the urban environment. Finally, 125 different simulation experiments in the urban environment demonstrate that JPS can search out the optimal and safety path successfully, and meanwhile, it has a lower time complexity compared with the Vector Field Histogram (VFH), the Rapidly Exploring Random Tree (RRT), A*, and the Probabilistic Roadmaps (PRM) algorithms. Furthermore, JPS is validated usefully in the structured urban environment.

Suggested Citation

  • Kaijun Zhou & Lingli Yu & Ziwei Long & Siyao Mo, 2017. "Local Path Planning of Driverless Car Navigation Based on Jump Point Search Method Under Urban Environment," Future Internet, MDPI, vol. 9(3), pages 1-13, September.
    • Handle: RePEc:gam:jftint:v:9:y:2017:i:3:p:51-:d:111704
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    Cited by:

    1. Choy, Siu-Kai & Yu, Carisa K.W. & Lee, Tanki C.L. & Lam, Benson S.Y. & Wong, Catherine Y.W., 2021. "A two-stage variational jump point detection algorithm for real estate analysis," Land Use Policy, Elsevier, vol. 111(C).

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