AI-Driven Intelligent Accident Prevention System Using Sensor Fusion and Vision- Based Road Analysis

Abstract

Abstract- Road accidents remain a major concern worldwide, often
caused by over speeding, poor road conditions, and traffic
violations. This paper presents an intelligent accident prevention
system that integrates artificial intelligence, camera-based vision,
and sensor technologies to enhance road safety. The proposed
system continuously monitors the driving environment using a
front-facing camera and proximity sensors to detect road anomalies
such as potholes, speed breakers, and traffic signals. The collected
data is processed through an AI-based framework incorporating
computer vision, sensor fusion, and decision-making algorithms to
enable adaptive speed control, automatic braking, and traffic signal
compliance.
The performance of the proposed system is evaluated using
standard metrics, achieving an accuracy of 94.2%, precision of
92.8%, recall of 93.5%, and an F1-score of 93.1%, indicating high
reliability in detecting road conditions and minimizing false
detections. Additionally, speed control analysis demonstrates
smooth and controlled deceleration, ensuring safe stopping
distances and improved driving stability. These results confirm the
effectiveness of the proposed approach in real-time accident
prevention and highlight its potential for integration into advanced
driver assistance systems and intelligent transportation
frameworks.