ArticleSensors (Basel, Switzerland)2021
Ensemble CNN to Detect Drowsy Driving with In-Vehicle Sensor Data.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Exploring the potential effects of different frequencies of flashing light stimulation on alleviating mental fatigue: a behavioral and EEG study.Frontiers in human neuroscience · 2026Article
- Lightweight and Real-Time Driver Fatigue Detection Based on MG-YOLOv8 with Facial Multi-Feature Fusion.Journal of imaging · 2025Article
- A Novel Neural Network Model Based on Real Mountain Road Data for Driver Fatigue Detection.Biomimetics (Basel, Switzerland) · 2025Article
- [Fatigue feature extraction and classification algorithm of forehead single-channel electroencephalography signals].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2024Article
- Research on Fatigued-Driving Detection Method by Integrating Lightweight YOLOv5s and Facial 3D Keypoints.Sensors (Basel, Switzerland) · 2023Article
- Inattentive Driving Detection Using Body-Worn Sensors: Feasibility Study.Sensors (Basel, Switzerland) · 2022Article
- Trends and Future Prospects of the Drowsiness Detection and Estimation Technology.Sensors (Basel, Switzerland) · 2021Review
- Demand-Driven Data Acquisition for Large Scale Fleets.Sensors (Basel, Switzerland) · 2021Article
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Authors and funding
3 authors.
Funding
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Abstract
Drowsy driving is a major threat to the safety of drivers and road traffic. Accurate and reliable drowsy driving detection technology can reduce accidents caused by drowsy driving. In this study, we present a new method to detect drowsy driving with vehicle sensor data obtained from the steering wheel and pedal pressure. From our empirical study, we categorized drowsy driving into long-duration drowsy driving and short-duration drowsy driving. Furthermore, we propose an ensemble network model composed of convolution neural networks that can detect each type of drowsy driving. Each subnetwork is specialized to detect long- or short-duration drowsy driving using a fusion of features, obtained through time series analysis. To efficiently train the proposed network, we propose an imbalanced data-handling method that adjusts the ratio of normal driving data and drowsy driving data in the dataset by partially removing normal driving data. A dataset comprising 198.3 h of in-vehicle sensor data was acquired through a driving simulation that includes a variety of road environments such as urban environments and highways. The performance of the proposed model was evaluated with a dataset. This study achieved the detection of drowsy driving with an accuracy of up to 94.2%.
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