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Autonomous driving is one of the great problems of artificial intelligence that has not yet been solved. Therefore, one of the methods applied to seek the solution to this problem is the development of algorithms on vehicle platforms at a scale of 1:10. Because of this, the Mexican Robotics Tournament (TMR) vigorously promotes the development of these technologies through challenges or tests that simulate real scenarios at a scale of 1:10. This includes vehicles, signs, vehicular crossings, etc. The article will present a hybrid methodological proposal based on classical digital image processing and the use of a Random Sample Consensus (RANSAC) artificial intelligence algorithm for lane detection in a vehicle at 1/10 scale. The proposal obtained an efficiency of more than 95% of lane detection, the algorithm was evaluated on the track of the Mexican Robotics Tournament 2024 (TMR), held at the Autonomous University of Nuevo León (UANL), by the Mexican Federation of Robotics (FMR), in which a first place was obtained in the Auto ModelCar category of the current year.

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Lane Direction by Implementing Random Sampling Algorithms

  • 会议论文

  • 发表日期：

    2025年

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  • Alan C. Quezada-Villalobos,
  • José M. Celaya-Padilla,
  • Huizilopoztli Luna-Garcia,
  • Javier Saldívar,
  • Luis Carlos Reveles Gómez,
  • Hamurabi Gamboa Rosales,
  • Juvenal Villanueva-Maldonado,
  • Rafael Reveles

摘要

Autonomous driving is one of the great problems of artificial intelligence that has not yet been solved. Therefore, one of the methods applied to seek the solution to this problem is the development of algorithms on vehicle platforms at a scale of 1:10. Because of this, the Mexican Robotics Tournament (TMR) vigorously promotes the development of these technologies through challenges or tests that simulate real scenarios at a scale of 1:10. This includes vehicles, signs, vehicular crossings, etc. The article will present a hybrid methodological proposal based on classical digital image processing and the use of a Random Sample Consensus (RANSAC) artificial intelligence algorithm for lane detection in a vehicle at 1/10 scale. The proposal obtained an efficiency of more than 95% of lane detection, the algorithm was evaluated on the track of the Mexican Robotics Tournament 2024 (TMR), held at the Autonomous University of Nuevo León (UANL), by the Mexican Federation of Robotics (FMR), in which a first place was obtained in the Auto ModelCar category of the current year.