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.
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
