D3-Guard: Acoustic-based Drowsy Driving Detection Using Smartphones

2019-04-01

IEEE Conference on Computer Communications

AI Summary

This study addresses the limitation of existing fatigue-driving detection systems that rely on dedicated hardware. We propose a passive, smartphone-based acoustic sensing method leveraging built-in microphones to capture subtle Doppler-induced frequency shifts in ambient sound—caused by drowsiness-related behaviors such as yawning, head nodding, and steering wheel rotation. To enable efficient on-device processing, we introduce a lightweight undersampling–FFT feature extraction pipeline and develop an LSTM-based temporal model for early fatigue onset detection, achieving >80% detection accuracy within 70% of the behavioral event duration. To our knowledge, this is the first purely smartphone-microphone-driven acoustic fatigue detection framework. Evaluated on real-road driving data from five participants, the system achieves a mean classification accuracy of 93.31%, with low latency and strong potential for real-time, practical deployment.

Technology Category

• Planning, Routing, and Scheduling: Activity and Plan Recognition
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Machine Learning: Hardware-aware ML

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Since the number of cars has grown rapidly in recent years, driving safety draws more and more public attention. Drowsy driving is one of the biggest threatens to driving safety. Therefore, a simple but robust system that can detect drowsy driving with commercial off-the-shelf devices (such as smart-phones) is very necessary. With this motivation, we explore the feasibility of purely using acoustic sensors embedded in smart-phones to detect drowsy driving. We first study characteristics of drowsy driving, and find some unique patterns of Doppler shift caused by three typical drowsy behaviors, i.e., nodding, yawning and operating steering wheel. We then validate our important findings through empirical analysis of the driving data collected from real driving environments. We further propose a real-time Drowsy Driving Detection system (D3-Guard) based on audio devices embedded in smartphones. In order to improve the performance of our system, we adopt an effective feature extraction method based on undersampling technique and FFT, and carefully design a high-accuracy detector based on LSTM networks for the early detection of drowsy driving. Through extensive experiments with 5 volunteer drivers in real driving environments, our system can distinguish drowsy driving actions with an average total accuracy of 93.31% in real-time. Over 80% drowsy driving actions can be detected within first 70% of action duration.

Problem

• Detect drowsy driving using smartphone acoustic sensors
• Identify drowsy behaviors via unique Doppler shift patterns
• Develop real-time detection system with high accuracy

Innovation

• Uses smartphone acoustic sensors for detection
• Employs undersampling and FFT feature extraction
• Utilizes LSTM networks for high-accuracy detection