A car following model with an attention-based cognitive framework: theory, application, and statistical analysis

buir.advisorYıldız, Yıldıray
dc.contributor.authorHabboush, Seymanur Al
dc.date.accessioned2024-08-23T13:43:40Z
dc.date.available2024-08-23T13:43:40Z
dc.date.copyright2024-08
dc.date.issued2024-08
dc.date.submitted2024-08-21
dc.descriptionCataloged from PDF version of article.
dc.descriptionThesis (Master's): Bilkent University, Department of Mechanical Engineering, İhsan Doğramacı Bilkent University, 2024.
dc.descriptionIncludes bibliographical references (leaves 47-52).
dc.description.abstractTraffic simulators are essential for testing autonomous driving algorithms, and they require driver models that accurately emulate human behavior to reflect real traffic conditions. This thesis focuses on developing human driver models to be used in these simulators. The limitations of fixed driver models, which do not adapt to new information, are addressed by introducing an attention-based learning mechanism inspired by human memory. This mechanism is integrated into a multi-type car-following model developed in this study. Unlike existing models, this approach allows the ego driver’s decisions to be influenced by both the vehicle in front and the driver behind them. The predictive capabilities of the proposed model is demonstrated using human driving data and a comprehensive statistical analysis of the model parameter distributions is provided. This analysis shows how the model captures general behavioral tendencies across different data sets, enhancing the understanding of interactions between human drivers and providing more realistic simulations for testing purposes. Finally, a step by step guide for using the model in the development of high-fidelity traffic models is presented.
dc.description.provenanceSubmitted by Betül Özen (ozen@bilkent.edu.tr) on 2024-08-23T13:43:40Z No. of bitstreams: 1 B162608.pdf: 3216041 bytes, checksum: ab6d49c58eaa30fe53e642014a647ac7 (MD5)en
dc.description.provenanceMade available in DSpace on 2024-08-23T13:43:40Z (GMT). No. of bitstreams: 1 B162608.pdf: 3216041 bytes, checksum: ab6d49c58eaa30fe53e642014a647ac7 (MD5) Previous issue date: 2024-08en
dc.description.statementofresponsibilityby Şeymanur Al Habboush
dc.format.extentxi, 52 leaves : charts ; 30 cm.
dc.identifier.itemidB162608
dc.identifier.urihttps://hdl.handle.net/11693/115762
dc.language.isoEnglish
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectHuman memory
dc.subjectLearning
dc.subjectAdaptation
dc.subjectCar following model
dc.subjectAdaptive control applications
dc.titleA car following model with an attention-based cognitive framework: theory, application, and statistical analysis
dc.title.alternativeDikkate dayalı bilişsel yapıya sahip bir araç takip modeli: teori, uygulama ve istatistiksel analiz
dc.typeThesis
thesis.degree.disciplineMechanical Engineering
thesis.degree.grantorBilkent University
thesis.degree.levelMaster's
thesis.degree.nameMS (Master of Science)

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