Enhancing Road Safety: Machine Learning-Based Car Orientation Detection for Code Violation Prevention

Abstract

The rising number of lives lost and serious injuries resulting from car accidents has underscored the urgent need for driving assistance systems and code violation detection mechanisms. In response to this pressing issue, in this study, we present a novel and advanced device specifically designed to detect road code violations, with a particular focus on the ”no entry” situations. Traditional approaches relying on GPS and offline maps have exhibited limitations in accuracy and real-time performance. To overcome these challenges, we propose a cutting-edge computer vision solution that leverages state-of-the-art machine learning models for accurate car orientation detection. Our system combines high- resolution single camera and on board processing units to continuously analyse the surrounding road environment in real-time. The machine learning model employed has been extensively trained on a vast dataset, enabling it to recognize ‘no entry’ signs and vehicles violating traffic rules.