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.
Navigating roundabouts is a complex driving scenario for both manual and autonomous vehicles. This paper proposes an approach based on the use of the Q-learning algorithm to train an autonomous vehicle agent to learn how to appropriately navigate roundabouts. The proposed learning algorithm is implemented using the CARLA simulation environment. Several simulations are performed to train the algorithm in two scenarios: navigating a roundabout with and without surrounding traffic. The results illustrate that the Q-learning-algorithm-based vehicle agent is able to learn smooth and efficient driving to perform maneuvers within roundabouts.
This article presents a methodology to reinforce the teaching of fundamentals of robotics to computer scientists. The pedagogical basis is focused on engaging students in an in-depth study of the subject using computing in a substantive way. The approach consists in complementing the lectures by programming assignments focused on giving students a deeper understanding of how robotic systems work from the inside. This article presents the author's experience in the use of this approach, as well as a set of proposed assignment projects. ß
Abstract-Remote laboratories have made significant progress during last few years. Their integration in engineering education helps solve many logistical difficulties inherent in conventional labs. In control engineering education, shifting from hands-on experience to remote experimentation completely modifies the learning environment. Also, factors that can compromise the effectiveness of learning outcomes need to be carefully considered. This short paper discusses a number of pedagogical limitations intrinsic to remote experimentation in control engineering education.
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