Research on the DV-Hop Location Algorithm Based on the Particle Swarm Optimization for the Automatic Driving Vehicle

  • Pei Huang
  • Xinjian XiangEmail author
  • Bingqiang Huang
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 890)


Localization is the foundation of automatic driving and it has always been a topic of research hotspot and difficult to deal with. The DV-Hop algorithm is most widely used in node localization research, and it will be used together with particle swarm optimization algorithm. In this paper, an NJPDH algorithm is proposed to compensate for the inferior accuracy of DV-Hop localization algorithm. Based on the DV-Hop location algorithm, the algorithm is added to the weight of each beacon node, the average distance is weighted, the particle swarm optimization is optimized from two aspects of the inertia weight and the active factor to avoid the particles trapped in the local optimization, and then get the location of the unknown nodes better. The simulation results show that under the same hardware conditions, compared with the particle swarm based PSO-DV-Hop algorithm, it can effectively reduce the impact of the jump distance, increase the coverage of the nodes, improve the positioning accuracy and robustness of the positioning process, and have better applicability.


Automatic driving vehicle location DV-Hop algorithm Particle swarm optimization Hop distance and inertia weighting 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.School of Mechanical and Energy EngineeringZhejiang University of Science and TechnologyHangzhouChina
  2. 2.School of Automation and Electrical EngineeringZhejiang University of Science and TechnologyHangzhouChina

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