Machine Learning Techniques for Undertaking Roundabouts in Autonomous Driving

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.

Autonomous Driving in Roundabout Maneuvers Using Reinforcement Learning with Q-Learning

“…The agent was trained for a total of 10,000 episodes, with each lasting for 100 samples or until a collision occurred. For training, roundabouts with no traffic were considered, and the speed of the vehicle was set up according to the predictive model obtained in [1]. For the two scenarios based on the simulated environment's recorded trajectories for the observed vehicle, the vehicle decision distribution is a ∈ ∆.…”

“…The proposed framework was evaluated using the metrics of performance previously cited to enter and exit roundabouts with and without traffic. The speed obtained from the predictive model [1] was adjusted in the trained model to the different segments of a roundabout, where convergence of the vehicle's behavior was observed within the simulation environment. During data collection, drivers involved in the experiment met the following conditions: (1) they used routes with roundabouts with different diameters, (2) they used single and multiple lanes; and (3) they used the same vehicle equipped for testing.…”

“…The approach was implemented using the CARLA simulation environment. Simulations carried out in this work used a set of naturalistic driving data from [1], including environmental information, as well as machine learning models for predicting steering angle and vehicle speed. The main contribution of this paper is the design of a tangible learning technique for sequential and automatic decision-making of autonomous vehicles through examples in a simulated environment.…”

“…The concept of defining the trajectory of the autonomous vehicle is based on the optimal control approach presented in [33] and successfully implemented in [34]. The viability of planned trajectories is guaranteed by imposing real predictions on the speed [1], acceleration, and exit of the roundabout.…”

“…One approach to understanding driving in roundabouts is through artificial intelligence and data mining techniques such as machine learning. For example, in [1] the authors presented rules of behavior to address a roundabout with an autonomous vehicle, modeling the behavior of a human driver through factors such as the speed of the vehicle, the angle of the wheel, the diameter of the roundabout, etc. The authors of [2] presented an adaptive tactical behavior planner (ATBP) for an autonomous vehicle, capable of planning behaviors similar to human drivers when navigating a roundabout.…”