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Machine Learning Techniques for Undertaking Roundabouts in Autonomous Driving.

Laura García Cuenca1, Javier Sanchez-Soriano2, Enrique Puertas3

  • 1Science, Computing and Technology, Universidad Europa de Madrid, Calle Tajo s/n, Villaviciosa de Odón, 28670 Madrid, Spain. laura.garcia@universidadeuropea.es.

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Summary
This summary is machine-generated.

This study introduces a machine learning method for autonomous vehicles to navigate roundabouts. It develops predictive models for speed and steering, creating actionable rules for safe and efficient autonomous driving maneuvers.

Keywords:
autonomous drivingdata miningdeep learninglinear regressionmachine learningroundaboutssensor fusionsupport vector machines

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Area of Science:

  • Robotics and Artificial Intelligence
  • Transportation Engineering
  • Machine Learning

Background:

  • Autonomous vehicles require sophisticated control systems for complex maneuvers like roundabouts.
  • Existing methods may lack the adaptability and precision needed for real-world roundabout navigation.

Purpose of the Study:

  • To develop a machine learning-based technique for autonomous vehicle roundabout navigation.
  • To generate actionable rules for vehicle speed and steering control during roundabout maneuvers.

Main Methods:

  • Utilized supervised learning algorithms including support vector machines, linear regression, and deep learning.
  • Built predictive models for vehicle speeds and steering angles using driver-vehicle interaction data.
  • Incorporated traffic environment data, including roundabout geometry and lane count from Open-Street-Maps and video processing.

Main Results:

  • Successfully generated rules of action for autonomous vehicle speed and steering.
  • Developed a predictive model capable of simulating roundabout maneuvers.
  • Demonstrated the feasibility of using machine learning for autonomous roundabout navigation.

Conclusions:

  • The proposed machine learning technique enables autonomous vehicles to perform complex roundabout maneuvers.
  • The generated rules provide a foundation for developing robust autonomous driving systems.
  • This approach enhances the safety and efficiency of autonomous vehicle navigation in intricate traffic scenarios.