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Cluster Computing

, Volume 22, Supplement 3, pp 7515–7523 | Cite as

Secondary parallel automatic parking of endpoint regionalization based on genetic algorithm

  • Bo SuEmail author
  • Junhan Yang
  • Lijuan Li
  • Yu Wang
Article
  • 130 Downloads

Abstract

Automatic parking technology can overcome the difficulties of drivers in tight spaces during parking. In this paper, in order to improve the accuracy of parallel parking in tight spaces, a secondary parallel automatic parking method of endpoint regionalization based on genetic algorithm is proposed. Firstly, a secondary parallel parking method of endpoint regionalization is proposed and a collision constraint function is established by planning the secondary parallel automatic parking path and analyzing the possible collisions during parking. Secondly, the secondary parallel parking path is planed by estimating the minimum parking length and designing a reasonable terminal area for parking. The simulation made on MATLAB verifies the feasibility of the method. In order to improve the efficiency of secondary parallel automatic parking of endpoint regionalization and achieve the shortest automatic parking path, a parking path function with constraint conditions is established in this paper and optimizing is done by taking the parking path function as target function with genetic algorithm. The simulation results show that the secondary parallel automatic parking of endpoint regionalization based on genetic algorithm can enable cars to park in the designed terminal area correctly without collision and the parking path is shortened by 4.1% compared with that of the original one.

Keywords

Automotive engineering Automatic parking Endpoint regionalization Genetic algorithm Parking path function Optimizing 

Notes

Acknowledgements

This work was supported by the Fundamental Research Funds for the Central Universities and Cultivating Fund of Xi’an University of Science and Technology (No. 201745).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Aerospace Science and TechnologyXidian UniversityXi’anChina
  2. 2.School of ComputerXi’an University of Science and TechnologyXi’anChina
  3. 3.LinkedIn CorporationSunnyvaleUSA

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