Development of a Testbed for Autonomous Vehicles: Integrating  MPC Control with Monocular Camera Lane Detection

Rahman, Shantanu; Hasin, Nayeb; Islam, Mainul

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dc.contributor.author	Rahman, Shantanu
dc.contributor.author	Hasin, Nayeb
dc.contributor.author	Islam, Mainul
dc.date.accessioned	2025-02-27T09:22:01Z
dc.date.available	2025-02-27T09:22:01Z
dc.date.issued	2024-06-27
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dc.identifier.uri	http://hdl.handle.net/123456789/2322
dc.description	Supervised by Professor Dr Golam Sarowar, Department of Electrical and Electronic Engineering (EEE) Islamic University of Technology (IUT) Board Bazar, Gazipur, Bangladesh This thesis is submitted in partial fulfillment of the requirement for the degree of Bachelor of Science in Electrical and Electronic Engineering, 2024	en_US
dc.description.abstract	Autonomous vehicles are becoming popular day by day not only for autonomous road traversal but also for industrial automation, farming and military. Most of the standard vehicles follow the Ackermann style steering mechanism. This has become to de facto standard for large and long faring vehicles. The local planner of an autonomous vehicle controls the low-level vehicle movement upon which the vehicle will perform its motor actuation. In our work, we focus on autonomous vehicles in road and perform experiments to analyze the effect of low-level controllers in the simulation and a real environment. To increase the precision and stability of trajectory tracking in autonomous cars, a novel method that combines lane identification with Model Predictive Control (MPC) is presented. The research focuses on camera-equipped autonomous vehicles and uses methods like edge recognition, sliding window based straight-line identification for lane line extraction, and dynamic region of interest (ROI) extraction. Next, to follow the identified lane line, an MPC built on a bicycle vehicle dynamics model is created. A single-lane road simulation model is built using ROS Gazebo and tested in order to verify the controller's performance. The root mean square error between the optimal tracking trajectory and the target trajectory was reduced by 27.65% in the simulation results, demonstrating the high robustness and flexibility of the developed controller.	en_US
dc.language.iso	en	en_US
dc.publisher	Department of Electrical and Elecrtonics Engineering(EEE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh	en_US
dc.subject	Autonomous, UGV, Ackermann, MPC, Control, Hardware, Computer vision	en_US
dc.title	Development of a Testbed for Autonomous Vehicles: Integrating MPC Control with Monocular Camera Lane Detection	en_US
dc.type	Thesis	en_US