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.

翻译：自动驾驶车辆日益普及，不仅用于自主道路行驶，还广泛应用于工业自动化、农业和军事领域。大多数标准车辆采用阿克曼式转向机制，这已成为大型长途车辆的事实标准。自动驾驶车辆的局部规划器控制底层车辆运动，车辆据此执行电机驱动。本研究聚焦于道路自动驾驶车辆，通过实验分析仿真和真实环境中底层控制器的影响。为提高自动驾驶汽车轨迹跟踪的精度和稳定性，提出了一种融合车道识别与模型预测控制（MPC）的新方法。该研究针对配备摄像头的自动驾驶车辆，采用边缘识别、基于滑动窗口的直线识别进行车道线提取，以及动态感兴趣区域（ROI）提取等技术。随后，为跟随识别出的车道线，建立了基于自行车车辆动力学模型的MPC控制器。利用ROS Gazebo构建单车道道路仿真模型并进行测试，以验证控制器性能。仿真结果显示，最优跟踪轨迹与目标轨迹之间的均方根误差降低了27.65%，证明了所开发控制器具有高度鲁棒性和灵活性。