Small size vehicle application with lane tracking capability via CHEVP algorithm

Year 2020, Volume: 9 Issue: 2, 76 - 85, 08.06.2020

Abdulhamit Sevgi , Emel Soylu , Raif Bayır

https://doi.org/10.18245/ijaet.639025

Abstract

Humans are living things that make mistakes. Failure of drivers while driving can cause life, cost, organ loss. Some accidents may remain traumatic in people's memory and adversely affect their future lives. Traffic rules have been developed to prevent such accidents. Traffic is formally organized in many jurisdictions, with marked lanes, junctions, intersections, interchanges, traffic signals, or signs. Rules of the road and driving etiquette are the general practices and procedures that road users are required to follow. Thanks to these guides placed on the roads, drivers can go on the road in harmony. Despite all these precautions, there are many traffic accidents caused by the driver's carelessness, sleeplessness and tiredness. In today's technology, it is possible to utilize methods of processing images from cameras located in the vehicle to minimize driver-induced accidents. In this study, a prototype system was established in order to use the technologies used in autonomous vehicles and to teach these technologies. Camera and computer are placed on a battery-powered vehicle. Using the OpenCV library, lane tracking is performed successfully using the Canny/Hough Estimation of Vanishing Points (CHEVP) method. The developed system is suitable for the use and development of image processing technologies that are used in autonomous vehicle technology. The system is tested in real-time on a designed runway. From the real-time experimental studies, high-performance results were obtained.

Keywords

Lane Tracking, CHEVP, autonomous vehicle

Supporting Institution

Karabük University, Scientific Research Projects

Project Number

KBÜ-BAP-18-YL-163

Thanks

Karabük University supported this study within the scope of Scientific Research Projects (KBÜ-BAP-18-YL-163). We would like to thank KBÜELAR electric vehicle and ROBOTAXI team for their contributions.

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