Self-Driving Cars and Lane Change Model Using Rayleigh Distribution

The present article introduces a MATLAB-based simulation framework for the implementation and assessment of a lane-change system for autonomous vehicles. The framework comprises algorithms for lane detection, decision-making about lane change execution, and display of the vehicle's actions. A synthetic dataset is created to replicate lane-marked roadways, offering a controlled setting for system testing and validation. The framework is additionally assessed utilizing performance criteria including accuracy, precision, and area under the curve (AUC). The validation includes parameter estimation, goodness-of-fit metrics, and graphical comparisons. The results indicate that the Rayleigh model outperforms the other models in capturing the characteristics of lane change behavior.