Table of Contents
Autonomous vehicles rely on various control systems to maintain safe and accurate lane positioning. Feedback control is a key technique used to ensure the vehicle stays within its lane despite external disturbances and uncertainties. This article explores a real-world case study demonstrating the application of feedback control in lane keeping for autonomous vehicles.
System Overview
The system uses sensors such as cameras and lidar to detect lane markings and the vehicle’s position relative to the lane center. The data collected is processed to generate a deviation signal, indicating how far the vehicle has drifted from its intended path. A feedback control algorithm then adjusts the steering to correct this deviation.
Control Algorithm
The control strategy implemented is a Proportional-Integral-Derivative (PID) controller. It computes the steering angle based on the current deviation, the accumulated error over time, and the rate of change of the error. This approach helps in reducing steady-state errors and improving response time.
Implementation Details
The feedback control system was integrated into the vehicle’s existing control architecture. The sensors provided real-time data at a frequency of 50 Hz. The PID controller parameters were tuned through iterative testing to optimize lane keeping performance under different driving conditions, such as curves and varying speeds.
Results and Observations
The case study demonstrated that the feedback control system effectively maintained lane position with minimal deviation. The vehicle responded accurately to sudden disturbances, such as crosswinds or uneven road surfaces. The system’s robustness was confirmed through extensive testing across various scenarios.