Design and Implementation of a ROS 2-Based Autonomous Driving System in a Simulated Environment

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dc.contributor.advisorMikuska, Róbert
dc.contributor.authorAwadelkarim, Ahmed Adil Ali
dc.contributor.departmentDE--Műszaki Kar
dc.date.accessioned2025-12-16T16:26:40Z
dc.date.available2025-12-16T16:26:40Z
dc.date.created2025-11-24
dc.description.abstractThe main objective of this thesis was to develop a modular perception and control system for autonomous vehicles, as well as validate it via testing in a realistic simulation environment (Gazebo/ROS), utilizing Ackerman steering kinematics. The primary focus of the research was to achieve robust real-time autonomous navigation on complex simulated tracks through the integration of computer vision and robotics. A central focus of the research was the Hybrid Computer Vision Pipeline, which provides the ability to balance high accuracy and computational efficiency for the vehicle. For lane-following, the system utilized fast heuristic methods, specifically HSL Color Segmentation and Canny Edge Detection to provide the vehicle's geometric data (e.g., curvature). Traffic sign detection was accomplished through a multi-step process consisting of Hough Gradient Methods and Haar Cascades to quickly localize signs, then a custom CNN to accurately classify them. Overall, the research demonstrated the critical tradeoff between efficiency and reliability in achieving electric mobility automation.
dc.description.courseMechatronical Engineeringen
dc.description.degreeBSc/BA
dc.format.extent72
dc.identifier.urihttps://hdl.handle.net/2437/400910
dc.language.isoen
dc.rights.infoHozzáférhető a 2022 decemberi felsőoktatási törvénymódosítás értelmében.
dc.subjectROS2
dc.subjectObject Recognition
dc.subjectAutonomous Vehicle
dc.subject.dspaceEngineering Sciences
dc.titleDesign and Implementation of a ROS 2-Based Autonomous Driving System in a Simulated Environment
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