Design and Implementation of an AI-Driven Driving Assessment Tool for Comprehensive Skill Evaluation

Abstract

This paper introduces an AI-driven driving assessment tool that evaluates driver performance using a combination of IoT sensors, computer vision, and machine learning techniques. The system continuously monitors key driving behaviors, including lane discipline, reaction time, and alertness, providing real-time, data-driven evaluations. The core components include a convolutional neural network (CNN) for lane detection, a fine-tuned VGG16 model for alertness monitoring, and a gradient boosting algorithm for behavioural scoring. The tool was tested on 50 drivers across diverse real-world conditions, achieving an overall accuracy of 92%. The findings highlight its potential applications in driver training, recruitment assessments, and road safety enforcement. Future enhancements will focus on expanding datasets, optimizing computational efficiency, and integrating real-time feedback mechanisms to further improve driving performance assessment.