A Real-Time Obstacle Avoidance Method for Autonomous Vehicles Using an Obstacle-Dependent Gaussian Potential Field

Jang Ho Cho, Dong Sung Pae, Myo Taeg Lim, Tae Koo Kang

Research output: Contribution to journalArticlepeer-review

39 Citations (Scopus)

Abstract

A new obstacle avoidance method for autonomous vehicles called obstacle-dependent Gaussian potential field (ODG-PF) was designed and implemented. It detects obstacles and calculates the likelihood of collision with them. In this paper, we present a novel attractive field and repulsive field calculation method and direction decision approach. Simulations and the experiments were carried out and compared with other potential field-based obstacle avoidance methods. The results show that ODG-PF performed the best in most cases.

Original languageEnglish
Article number5041401
JournalJournal of Advanced Transportation
Volume2018
DOIs
Publication statusPublished - 2018

Bibliographical note

Funding Information:
This work was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2016R1D1A1B01016071 and NRF-2016R1D1A1B 03936281) and also in part by the Brain Korea 21 Plus Project in 2018.

Publisher Copyright:
© 2018 Jang-Ho Cho et al.

ASJC Scopus subject areas

  • Automotive Engineering
  • Economics and Econometrics
  • Mechanical Engineering
  • Computer Science Applications
  • Strategy and Management

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