Citation

García Cuenca L, Sanchez-Soriano J, Puertas E, Fernandez Andrés J, Aliane N. Sensors (Basel) 2019; 19(10): s19102386.

Affiliation

Science, Computing and Technology, Universidad Europa de Madrid, Calle Tajo s/n, Villaviciosa de Odón, 28670 Madrid, Spain. nourdine.aliane@universidadeuropea.es.

Copyright

(Copyright © 2019, MDPI: Multidisciplinary Digital Publishing Institute)

DOI

10.3390/s19102386

PMID

31137714

Abstract

This article presents a machine learning-based technique to build a predictive model and generate rules of action to allow autonomous vehicles to perform roundabout maneuvers. The approach consists of building a predictive model of vehicle speeds and steering angles based on collected data related to driver-vehicle interactions and other aggregated data intrinsic to the traffic environment, such as roundabout geometry and the number of lanes obtained from Open-Street-Maps and offline video processing. The study systematically generates rules of action regarding the vehicle speed and steering angle required for autonomous vehicles to achieve complete roundabout maneuvers. Supervised learning algorithms like the support vector machine, linear regression, and deep learning are used to form the predictive models.

Language: en

Keywords

autonomous driving; data mining; deep learning; linear regression; machine learning; roundabouts; sensor fusion; support vector machines