CA MODELLING AND IMPROVED ALGORITHM: SAFE AND EFFICIENT DECISION MAKING FOR AUTONOMOUS DRIVING SYSTEM

dc.contributor.advisor	Almusawi, Husam Abdulkareem
dc.contributor.author	Tao, Yufei
dc.contributor.department	DE--Műszaki Kar
dc.date.accessioned	2025-09-04T14:53:48Z
dc.date.available	2025-09-04T14:53:48Z
dc.date.created	2025
dc.description.abstract	This study proposes a multi-model combinatorial congestion mitigation approach to solve the traffic problem of autonomous cars. The proposed model framework consists of three parts: a road modelling module based on meta cellular automata, a traffic flow speed prediction module consisting of an optimised long and short-term memory algorithm using a sparrow search algorithm, and a following distance prediction module that determines the optimal safe following distance using an adaptive cruise control algorithm. In simulation, this intelligent traffic model for autonomous cars can be applied to various complex traffic scenarios to improve traffic efficiency while reducing the collision risk of autonomous cars.
dc.description.course	Mechatronical Engineering	en
dc.description.degree	MSc/MA
dc.format.extent	78
dc.identifier.uri	https://hdl.handle.net/2437/397234
dc.language.iso	en
dc.rights.info	Hozzáférhető a 2022 decemberi felsőoktatási törvénymódosítás értelmében.
dc.subject	insertintelligent transportation system
dc.subject	machine learning algorithms
dc.subject	traffic safety
dc.subject.dspace	Engineering Sciences
dc.title	CA MODELLING AND IMPROVED ALGORITHM: SAFE AND EFFICIENT DECISION MAKING FOR AUTONOMOUS DRIVING SYSTEM

Megjelenítve 1 - 2 (Összesen 2)

Megjelenítve 1 - 1 (Összesen 1)