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

Engineering notes

Key topics: autonomous driving, sensor fusion, adas, control. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original 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. Keywords- Artificial Intelligence, Accident Prevention System, Computer Vision, Sensor Fusion, Road Safety, Speed Control, Traffic Signal Detection, Autonomous Driving, Intelligent Transportation Systems, ADAS.

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