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

doi:10.1155/2018/5041401

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

School of Electrical Engineering

Research output: Contribution to journal › Article › peer-review

34 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 language	English
Article number	5041401
Journal	Journal of Advanced Transportation
Volume	2018
DOIs	https://doi.org/10.1155/2018/5041401
Publication status	Published - 2018

ASJC Scopus subject areas

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

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

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.",

author = "Cho, {Jang Ho} and Pae, {Dong Sung} and Lim, {Myo Taeg} and Kang, {Tae Koo}",

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: {\textcopyright} 2018 Jang-Ho Cho et al.",

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AU - Kang, Tae Koo

N1 - 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.

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AB - 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.

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

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