Move Over Law Compliance Analysis Utilizing a Deep Learning Computer Vision Approach

Abstract

This paper presents the results of the Move Over law compliance study. This study was carried out for The Federal Highway Administration in cooperation with ten State Highway agencies that provided the data (video recordings). This paper describes an outline of the system that was invented, developed, and applied to determine Move Over law compliance, as well as the initial analysis of the impact of various factors on compliance. In order to carry out the analysis, we processed 68 videos that contained over 33,000 vehicles. The median compliance with the Move Over law was 42.5% and varied heavily depending on diverse factors. This study makes two key contributions: first, it introduces an automated deep learning-based system that detects and evaluates Move Over law compliance by leveraging object detection and tracking technologies. Second, it presents a large-scale, multi-state compliance assessment, providing new empirical insights into driver behavior across various incident conditions. These findings offer a data-driven foundation for refining Move Over laws, enhancing public awareness efforts, and improving enforcement strategies.