Accident Prevention System for Cars, Buses, Trucks
DOI: 10.22214/ijraset.2024.64472
archive: archived pipeline: cataloged verified
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Summary
This paper addresses the critical safety issue of driver drowsiness, a leading cause of road accidents, particularly in long-distance travel and commercial transportation sectors. The authors propose a real-time Driver Drowsiness Detection System designed to identify early signs of fatigue and issue timely alerts to prevent crashes. The motivation stems from the high incidence of accidents caused by sleep deprivation, which accounts for approximately 20% of road incidents, and the need for a non-intrusive, proactive solution that can be integrated into personal vehicles and commercial fleets. The system is implemented using a Raspberry Pi (3B+ or 4) as the central processing unit, connected to a camera module for real-time video capture. The software architecture utilizes Python libraries, including OpenCV, Dlib, TensorFlow, or Keras, to perform image processing and deep learning tasks. The core detection mechanism relies on facial landmark detection to monitor specific behavioral indicators of drowsiness, such as eye closure, head position, yawning, and blink rate. Specifically, the system calculates the Eye Aspect Ratio (EAR) to determine if the driver’s eyes are closed. If the EAR falls below a predefined threshold for a sustained period, the system classifies the driver as drowsy. Upon detection, the system triggers immediate alerts through audio warnings (buzzers) or visual signals (LEDs). The implementation process involves setting up the Raspberry Pi, connecting the camera, deploying pre-trained models for facial feature detection, and testing the system under various lighting conditions to ensure reliability. The paper outlines the system’s potential to significantly reduce accident-related fatalities and injuries by providing continuous monitoring and feedback. While the current implementation focuses on visual cues and basic alert mechanisms, the authors note that the system is extensible. Future enhancements could include cloud-based data analysis, multi-modal sensing systems, and the integration of more accurate physiological sensors, such as heart rate monitors or EEG devices, to improve detection reliability. The study also acknowledges challenges such as cost, privacy concerns, and integration with existing vehicle infrastructure. However, the authors conclude that the benefits of saving lives and reducing accident costs outweigh these obstacles. The proposed system represents a feasible step toward standardizing driver safety technologies, with applications extending to semi-automated vehicles and broader transportation safety initiatives.
Provenance
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-06-18 |
| archive | success | canonical_url | — | — | 1 | 2026-06-25 |
| extract | success | cached | — | — | 2 | 2026-06-26 |
| clean | success | clean | — | — | 1 | 2026-06-18 |
| chunk | success | chunk | — | — | 1 | 2026-06-18 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-18 |
| promote | success | — | — | — | 1 | 2026-06-18 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-26 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-18 |
| verify | success | — | — | — | 1 | 2026-06-26 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.
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