Sleep Detection System for Trucks: A Real-Time Multi-Modal Data-Driven AI Framework for Driver Fatigue Monitoring

Abstract

Fatigue-induced driving remains one of the most severe and under-addressed threats to road safety, particularly among commercial truck drivers tasked with long, monotonous routes. In response to rising accident statistics and increasing demand for proactive driver monitoring, this research proposes a comprehensive, multi-modal fatigue detection system specifically designed for real-time deployment in heavy vehicle environments. The proposed system fuses three primary input modalities: behavioral signals (Eye Aspect Ratio – EAR), physiological indicators (Heart Rate Variability – HRV), and vehicular behavior (Steering Entropy – SE). A hybrid deep learning architecture is employed, combining Convolutional Neural Networks (CNN) for spatial feature extraction with Long Short-Term Memory (LSTM) networks to capture temporal fatigue trends. Data collected through camera systems, HRV sensors, and CAN-bus telemetry are processed in real-time to generate fatigue alerts before critical drowsiness thresholds are crossed. Evaluation of the model reveals a classification accuracy of 93%, with precision, recall, and F1-score exceeding 90% across key scenarios. Latency analysis confirms system responsiveness with an average detection time of ~120 ms, making it suitable for real-world deployment. Additionally, visual analysis techniques—including feature contribution plots, clustering views, and PCA projections—are incorporated to improve transparency and interpretability of the model’s decision-making pipeline. This research directly supports United Nations Sustainable Development Goal (UN SDG) 3.6, which aims to halve the number of global deaths and injuries from road traffic accidents by 2030. The proposed solution contributes to this goal by offering a low-latency, high-accuracy, and interpretable model capable of scaling across commercial fleets.The implementation is available on the presented GitHub repository, https://github.com/Paras-Vermaa/Drowsy-Driver-Detection

Introduction

1. Background

• Drowsy driving is a critical and underestimated cause of road accidents globally.

• WHO estimates 1.3 million road deaths yearly, with fatigue being a major contributor, especially among long-haul truck drivers.

• In the U.S., over 100,000 crashes/year are caused by fatigue. In India, 40% of highway crashes involve drowsy driving.

• Existing systems are inadequate for detecting early signs of fatigue, especially in commercial trucking.

2. Problem Statement

• Current systems (e.g., steering/lane monitoring) lack accuracy and are prone to false positives due to external and driver-specific factors.

• There's a need for a multi-modal, real-time detection system that combines behavioral, physiological, and vehicular data for improved accuracy.

3. Objectives

• Develop a hybrid AI model using:

  • Visual monitoring (e.g., eye closure, yawns)

  • Physiological data (e.g., HRV)

  • Vehicle dynamics (e.g., steering, lane drift)

• Build a real-time alerting mechanism (buzzer, seat vibration).

• Ensure scalability, privacy, and low-latency operation.

4. Scope and Significance

• Focused on truck drivers, but adaptable to aviation, railways, and mining.

• Uses affordable hardware, open-source tools, and modular design.

• Supports UN SDG 3.6, aiming to reduce road traffic deaths by 50% by 2030.

5. Literature Review

Drowsiness Statistics

• Up to 20% of global crashes involve fatigue.

• In the EU, it accounts for 15–25% of fatal accidents.

• Studies confirm commercial truckers are most at risk.

Existing Detection Methods

• Hybrid systems (e.g., eye + HRV + steering) show 93%+ accuracy in trials.

• Gaps: Single-sensor systems fail under varied conditions; few solutions are optimized for trucks.

6. Methodology

System Architecture

• A hybrid, edge-computing system using:

  • IR Camera for facial behavior (blink, yawn, head tilt)

  • HRV Sensor (e.g., wrist PPG)

  • CAN Bus Interface for vehicle dynamics

  • ML Fusion Engine (CNN + LSTM) for decision-making

  • Alert System (buzzer, seat vibration, display)

Key Inputs

• Behavioral Monitoring:

  • Uses EAR (Eye Aspect Ratio) and MAR (Mouth Aspect Ratio)

  • Trigger alerts after sustained eye closure/yawning

• Physiological Monitoring:

  • Analyzes HRV metrics like RMSSD and LF/HF ratio

  • HRV drop indicates fatigue onset

• Vehicle Behavior Analysis:

  • Calculates Steering Entropy (SE) — randomness in steering as a fatigue signal

  • Also monitors lane drift and pedal usage

Conclusion

Fatigue-related accidents among long-haul truck drivers continue to pose a significant threat to road safety worldwide. Despite available detection technologies in the consumer automotive sector, the trucking industry lacks a robust, scalable, and truck-optimized system that can operate accurately in the demanding conditions of commercial transport. This research presented the design, implementation, and evaluation of a hybrid AI-powered drowsiness detection system, integrating: • Computer vision techniques for real-time facial behavior analysis • Physiological signal processing via HRV metrics • Vehicle telemetry monitoring to capture behavioral anomalies Our model, built on a CNN-LSTM architecture, demonstrated a detection accuracy of 94.1%, with response latency under 130ms, meeting industry-grade responsiveness requirements. The system operates efficiently on edge devices such as Jetson Nano, ensuring feasibility for in-vehicle deployment without cloud dependency. What sets this system apart is its multi-modality, allowing it to adapt to real-world variables such as poor lighting, face obstructions, and driver-specific variations. Further, its privacy-by-design approach ensures ethical compliance across jurisdictions.

References

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Copyright

Copyright © 2025 Paras Verma. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.