Artificial potential field method for obstacle avoidance and lane keeping

Research Article
Open access

Artificial potential field method for obstacle avoidance and lane keeping

Chenjie Wu 1*
  • 1 Maynooth International College of Engineering, Fuzhou University, Fuzhou, Fujian, 350108, China.    
  • *corresponding author 18650302819@163.com
ACE Vol.5
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-57-7
ISBN (Online): 978-1-915371-58-4

Abstract

This paper uses artificial potential field method to solve obstacle avoidance and lane keeping of autonomous driving safety assistance systems. It focuses on the lane line, direction, car obstacle, and static obstacle, which the car must pay attention to on the road. The artificial potential field method can assign different potential functions to different types of obstacles and road structures. This method also considers safety distance, limited speed, traffic rules, and safety problems. Superimposing these conditional functions will eventually result in a system that will continue to return the safety and appropriate next point until reaching the set goal point. Experiments used PyCharm to simulate a one-way, two-lane environment. The final results showed that regardless of the vehicle's initial position, the vehicle could return to the center of the regular lane and effectively avoid static and dynamic obstacles, in addition to controlling its speed to maintain a safe distance from obstacles. The result of experiments also proved that the artificial potential field method could be effectively used in the obstacle avoidance and lane keeping of automatic driving, which means that such a simple algorithm can also be installed on more lightweight navigation devices that require real-time dynamic obstacle avoidance.

Keywords:

autonomous driving, obstacle avoidance, path planning, artificial

Wu,C. (2023). Artificial potential field method for obstacle avoidance and lane keeping. Applied and Computational Engineering,5,559-565.
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References

[1]. J Li, X Mei, D Prokhorov and D Tao 2017 Deep neural network for structural prediction and lane detection in traffic scene Neural Networks and Learning Systems 28 690-703

[2]. J Barraquand, B Langlois and J C Latombe 1992 Numerical potential field techniques for robot path planning Systems, Man, and Cybernetics 22 224-241

[3]. Y K Hwang and N Ahuja 1992 A potential field approach to path planning Robotics and Automation 8 23-32

[4]. Y Rasekhipour, A Khajepour, S K Chen and B Litkouhi 2017 A potential field-based model predictive path-planning controller for autonomous road vehicles Intelligent Transportation Systems 18 1255-1267

[5]. G Li, A Yamashita, H Asama and Y Tamura 2012 An efficient improved artificial potential field based regression search method for robot path planning Mechatronics and Automation (2012 IEEE International Conference) pp.1227-1232

[6]. J Borenstein and Y Koren 1989 Real-time obstacle avoidance for fast mobile robots Systems, Man, and Cybernetics 19 pp. 1179-1187

[7]. J Ji, A Khajepour, W W Melek and Y Huang 2017 Path planning and tracking for vehicle collision avoidance based on model predictive control with multiconstraints Vehicular Technology 66 952-964

[8]. Z Li, S Li, Z Li, G Cui and X Wu 2018 Lane keeping of intelligent vehicle under crosswind based on visual navigation (International Conference on Information Science and Control Engineering) (ICISCE)

[9]. Y S Son, W Kim, S H Lee and C C Chung 2015 Robust multirate control scheme with predictive virtual lanes for the lane-keeping system of autonomous highway driving Vehicular Technology 64 3378-3391

[10]. R Toledo Moreo and M A Zamora-Izquierdo 2019 IMM-based lane-change prediction in highways with low-cost GPS/INS Intelligent Transportation Systems 10 pp.180-185

[11]. M T Wolf and J W Burdick 2008 Artificial potential functions for highway driving with collision avoidance Robotics and Automation (2008 IEEE International Conference) pp. 3731-3736

Cite this article

Wu,C. (2023). Artificial potential field method for obstacle avoidance and lane keeping. Applied and Computational Engineering,5,559-565.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

ISBN：978-1-915371-57-7(Print) / 978-1-915371-58-4(Online)
Editor：Omer Burak Istanbullu
Conference website: http://www.confspml.org
Conference date: 25 February 2023
Series: Applied and Computational Engineering
Volume number: Vol.5
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. J Li, X Mei, D Prokhorov and D Tao 2017 Deep neural network for structural prediction and lane detection in traffic scene Neural Networks and Learning Systems 28 690-703

[2]. J Barraquand, B Langlois and J C Latombe 1992 Numerical potential field techniques for robot path planning Systems, Man, and Cybernetics 22 224-241

[3]. Y K Hwang and N Ahuja 1992 A potential field approach to path planning Robotics and Automation 8 23-32

[4]. Y Rasekhipour, A Khajepour, S K Chen and B Litkouhi 2017 A potential field-based model predictive path-planning controller for autonomous road vehicles Intelligent Transportation Systems 18 1255-1267

[5]. G Li, A Yamashita, H Asama and Y Tamura 2012 An efficient improved artificial potential field based regression search method for robot path planning Mechatronics and Automation (2012 IEEE International Conference) pp.1227-1232

[6]. J Borenstein and Y Koren 1989 Real-time obstacle avoidance for fast mobile robots Systems, Man, and Cybernetics 19 pp. 1179-1187

[7]. J Ji, A Khajepour, W W Melek and Y Huang 2017 Path planning and tracking for vehicle collision avoidance based on model predictive control with multiconstraints Vehicular Technology 66 952-964

[8]. Z Li, S Li, Z Li, G Cui and X Wu 2018 Lane keeping of intelligent vehicle under crosswind based on visual navigation (International Conference on Information Science and Control Engineering) (ICISCE)

[9]. Y S Son, W Kim, S H Lee and C C Chung 2015 Robust multirate control scheme with predictive virtual lanes for the lane-keeping system of autonomous highway driving Vehicular Technology 64 3378-3391

[10]. R Toledo Moreo and M A Zamora-Izquierdo 2019 IMM-based lane-change prediction in highways with low-cost GPS/INS Intelligent Transportation Systems 10 pp.180-185

[11]. M T Wolf and J W Burdick 2008 Artificial potential functions for highway driving with collision avoidance Robotics and Automation (2008 IEEE International Conference) pp. 3731-3736

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