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

TL;DR

This paper presents a novel lane detection and control method combining monocular camera processing with Model Predictive Control to improve trajectory tracking in autonomous vehicles, validated through simulation.

Contribution

It introduces a new integrated approach using lane detection and MPC control based on vehicle dynamics for autonomous driving.

Findings

Root mean square error reduced by 27.65% in simulation

High robustness and flexibility demonstrated in control performance

Effective lane detection using edge recognition and ROI methods

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.