ArticleScientific data2024
A multimodal physiological dataset for driving behaviour analysis.
Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Multimodal Physiological Assessment of Attentional States and Driving Styles in Simulated Driving.Sensors (Basel, Switzerland) · 2026Article
- A Full-Chain Dataset for Intelligent New Energy Vehicles based on High-fidelity Digital Twin Platform.Scientific data · 2026Article
- A Human-Centered Multimodal Framework for Characterizing Safety-Relevant Driver Functional Domains: An Exploratory Study of Professional Bus Drivers.Sensors (Basel, Switzerland) · 2026Article
- Dataset of physiological signals in the use of advanced driver assistance systems (ADAS).Data in brief · 2026Article
- Machine-Learning-Enabled Hydrogel Biosensors for Wearable Health Monitoring.Gels (Basel, Switzerland) · 2026Review
- An EEG dataset for understanding driving expertise from naturalistic urban road experiments.Scientific data · 2026Article
- A multimodal drowsiness dataset using video, biometric, and behavioral data.Scientific data · 2026Article
- Multimodal Phenotyping Dataset of Driving Fatigue.Scientific data · 2026Article
- AI-Driven Smart Cockpit: Monitoring of Sudden Illnesses, Health Risk Intervention, and Future Prospects.Sensors (Basel, Switzerland) · 2025Review
- A Multidimensional Benchmark of Public EEG Datasets for Driver State Monitoring in Brain-Computer Interfaces.Sensors (Basel, Switzerland) · 2025Review
- A Dataset on Takeover during Distracted L2 Automated Driving.Scientific data · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Physiological signal monitoring and driver behavior analysis have gained increasing attention in both fundamental research and applied research. This study involved the analysis of driving behavior using multimodal physiological data collected from 35 participants. The data included 59-channel EEG, single-channel ECG, 4-channel EMG, single-channel GSR, and eye movement data obtained via a six-degree-of-freedom driving simulator. We categorized driving behavior into five groups: smooth driving, acceleration, deceleration, lane changing, and turning. Through extensive experiments, we confirmed that both physiological and vehicle data met the requirements. Subsequently, we developed classification models, including linear discriminant analysis (LDA), MMPNet, and EEGNet, to demonstrate the correlation between physiological data and driving behaviors. Notably, we propose a multimodal physiological dataset for analyzing driving behavior(MPDB). The MPDB dataset's scale, accuracy, and multimodality provide unprecedented opportunities for researchers in the autonomous driving field and beyond. With this dataset, we will contribute to the field of traffic psychology and behavior.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.