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.
Highlights
At the beginning of 2015, the UK considered the possibility of autonomous vehicles circulating in a shared traffic and controlled traffic environment for the first time
A machine-learning approach was used to build a predictive model to estimate the vehicle speed and steering angle, and to subsequently generate rules of action to be used by autonomous vehicles to perform roundabout maneuvers
Two different sets of data were used to model driver behavior: raw data acquired from an on-board instrumentation related to driver–vehicle interactions and aggregated data intrinsic to the traffic environment, such as roundabout geometry or the number of lanes obtained from Open-Street-Maps and offline video processing
Summary
At the beginning of 2015, the UK considered the possibility of autonomous vehicles circulating in a shared traffic and controlled traffic environment for the first time. By the end of the same year, the Spanish traffic agency (DGT) had launched an administrative instruction (15/V-113) [1] allowing the tests for research on self-driving vehicles to be carried out on roads. This is allowed in many other countries in the word, where several international organizations, such as SAE (Society of Automotive Engineers) [2]. USDOT (United States Department of Transportation) [3], have defined frameworks for autonomous vehicles Among these frameworks, automated driving is divided into five levels according to the level of human intervention, where the fifth level corresponds to a completely autonomous vehicle without a driver.
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