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 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.

Wildlife monitoring has entered a transformative era with the convergence of drone technology and artificial intelligence (AI). Drones provide access to remote and dangerous habitats, while AI unlocks the potential to process vast amounts of wildlife data. This synergy is reshaping wildlife monitoring, offering novel solutions to tackle challenges in species identification, animal tracking, anti-poaching, population estimation, and habitat analysis. This paper conducts a comprehensive literature review to examine the recent advancements in drone and AI systems for wildlife monitoring, focusing on two critical dimensions: (1) Methodologies, algorithms, and applications, analyzing the AI techniques employed in wildlife monitoring, including their operational frameworks and real-world implementations. (2) Challenges and opportunities, identifying current limitations, including technical hurdles and regulatory constraints, as well as exploring the untapped potential in drone and AI integration to enhance wildlife monitoring and conservation efforts. By synthesizing these insights, this paper will provide researchers with a structured framework for leveraging drone and AI systems in wildlife monitoring, identifying best practices and outlining actionable pathways for future innovation in the field.

This survey provides a comprehensive comparison of prominent open-source unmanned aerial vehicle (UAV) autopilots, focusing on their hardware compatibility, software features, and communication protocols. Additionally, it assesses the impact of these autopilots on research and education by examining their potential for integration with companion computers, compatibility with robot operating system (ROS) middleware and the MATLAB/Simulink environment, and the availability of simulation-in-the-loop (SITL) and hardware-in-the-loop (HITL) simulation tools. The paper concludes with a discussion of the advantages and disadvantages of these leading open-source autopilots.

This paper presents an interactive module for learning both the fundamental and practical issues of servo systems. This module, developed using Simulink in conjunction with the Matlab graphical user interface (Matlab-GUI) tool, is used to supplement conventional lectures in control engineering and robotics subjects. First, the paper introduces the theoretical background of servo systems. Then, the interactive module is presented, with a description of its main features as well some hints on how to integrate Simulink models within Matlab-GUI. Some of the module's capabilities are illustrated through classroom examples. Finally, the experience of putting this into use and student assessment of the tool are also addressed.

This paper describes the framework and components of an experimental platform for an advanced driver assistance system (ADAS) aimed at providing drivers with a feedback about traffic violations they have committed during their driving. The system is able to detect some specific traffic violations, record data associated to these faults in a local data-base, and also allow visualization of the spatial and temporal information of these traffic violations in a geographical map using the standard Google Earth tool. The test-bed is mainly composed of two parts: a computer vision subsystem for traffic sign detection and recognition which operates during both day and nighttime, and an event data recorder (EDR) for recording data related to some specific traffic violations. The paper covers firstly the description of the hardware architecture and then presents the policies used for handling traffic violations.