Autonomous (model sized) vehicles development with deep learning solutions

A tétel rövid nézete
dc.contributor.advisorKovács, László
dc.contributor.authorShiekh, Zeeshan Ahmad
dc.contributor.departmentDE--Informatikai Kar
dc.date.accessioned2023-04-19T14:36:40Z
dc.date.available2023-04-19T14:36:40Z
dc.date.created2023-04-19
dc.description.abstractThis thesis explores the use of deep learning techniques for creating an autonomous car, specifically utilizing a camera and OpenCV for lane detection and steering angle prediction. While the study demonstrates the potential for DL in improving the accuracy and reliability of autonomous vehicles, several challenges and limitations must be addressed, including training process optimization, model robustness, and ethical and social implications. This work lays the foundation for future research in autonomous vehicle development and may hasten the introduction of dependable autonomous cars, altering transportation networks and mobility.
dc.description.correctorN.I.
dc.description.courseComputer Science
dc.description.degreeMSc/MA
dc.format.extent52
dc.identifier.urihttps://hdl.handle.net/2437/350247
dc.language.isoen
dc.rights.accessHozzáférhető a 2022 decemberi felsőoktatási törvénymódosítás értelmében.
dc.subjectAutonomous vehicles
dc.subjectDeep learning
dc.subjectConvolutional neural networks (CNN)
dc.subjectComputer vision
dc.subjectDonkey Car
dc.subject.dspaceDEENK Témalista::Informatika::Számítógéptudomány
dc.titleAutonomous (model sized) vehicles development with deep learning solutions
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