Development of a Networked Multi-Participant Driving Simulator with Synchronized EEG and Telemetry for Traffic Research
This paper presents a multi-participant driving simulation framework designed to support traffic experiments involving the simultaneous collection of vehicle telemetry and cognitive data. The system integrates motion-enabled driving cockpits, high-fidelity steering and pedal systems, immersive visua...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Systems |
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| Online Access: | https://www.mdpi.com/2079-8954/13/7/564 |
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| author | Poorendra Ramlall Ethan Jones Subhradeep Roy |
| author_facet | Poorendra Ramlall Ethan Jones Subhradeep Roy |
| author_sort | Poorendra Ramlall |
| collection | DOAJ |
| description | This paper presents a multi-participant driving simulation framework designed to support traffic experiments involving the simultaneous collection of vehicle telemetry and cognitive data. The system integrates motion-enabled driving cockpits, high-fidelity steering and pedal systems, immersive visual displays (monitor or virtual reality), and the Assetto Corsa simulation engine. To capture cognitive states, dry-electrode EEG headsets are used alongside a custom-built software tool that synchronizes EEG signals with vehicle telemetry across multiple drivers. The primary contribution of this work is the development of a modular, scalable, and customizable experimental platform with robust data synchronization, enabling the coordinated collection of neural and telemetry data in multi-driver scenarios. The synchronization software developed through this study is freely available to the research community. This architecture supports the study of human–human interactions by linking driver actions with corresponding neural activity across a range of driving contexts. It provides researchers with a powerful tool to investigate perception, decision-making, and coordination in dynamic, multi-participant traffic environments. |
| format | Article |
| id | doaj-art-cf837c8161cf496babb939b2abf08f8b |
| institution | Kabale University |
| issn | 2079-8954 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Systems |
| spelling | doaj-art-cf837c8161cf496babb939b2abf08f8b2025-08-20T03:32:18ZengMDPI AGSystems2079-89542025-07-0113756410.3390/systems13070564Development of a Networked Multi-Participant Driving Simulator with Synchronized EEG and Telemetry for Traffic ResearchPoorendra Ramlall0Ethan Jones1Subhradeep Roy2Department of Mechanical Engineering, Embry-Riddle Aeronautical University, 1 Aerospace Blvd, Daytona Beach, FL 32114, USADepartment of Mechanical Engineering, Embry-Riddle Aeronautical University, 1 Aerospace Blvd, Daytona Beach, FL 32114, USADepartment of Mechanical Engineering, Embry-Riddle Aeronautical University, 1 Aerospace Blvd, Daytona Beach, FL 32114, USAThis paper presents a multi-participant driving simulation framework designed to support traffic experiments involving the simultaneous collection of vehicle telemetry and cognitive data. The system integrates motion-enabled driving cockpits, high-fidelity steering and pedal systems, immersive visual displays (monitor or virtual reality), and the Assetto Corsa simulation engine. To capture cognitive states, dry-electrode EEG headsets are used alongside a custom-built software tool that synchronizes EEG signals with vehicle telemetry across multiple drivers. The primary contribution of this work is the development of a modular, scalable, and customizable experimental platform with robust data synchronization, enabling the coordinated collection of neural and telemetry data in multi-driver scenarios. The synchronization software developed through this study is freely available to the research community. This architecture supports the study of human–human interactions by linking driver actions with corresponding neural activity across a range of driving contexts. It provides researchers with a powerful tool to investigate perception, decision-making, and coordination in dynamic, multi-participant traffic environments.https://www.mdpi.com/2079-8954/13/7/564driver behavior modelingEEG synchronizationhuman-in-the-loop simulationhyperscanningmulti-participant driving simulationnetworked driving simulator |
| spellingShingle | Poorendra Ramlall Ethan Jones Subhradeep Roy Development of a Networked Multi-Participant Driving Simulator with Synchronized EEG and Telemetry for Traffic Research Systems driver behavior modeling EEG synchronization human-in-the-loop simulation hyperscanning multi-participant driving simulation networked driving simulator |
| title | Development of a Networked Multi-Participant Driving Simulator with Synchronized EEG and Telemetry for Traffic Research |
| title_full | Development of a Networked Multi-Participant Driving Simulator with Synchronized EEG and Telemetry for Traffic Research |
| title_fullStr | Development of a Networked Multi-Participant Driving Simulator with Synchronized EEG and Telemetry for Traffic Research |
| title_full_unstemmed | Development of a Networked Multi-Participant Driving Simulator with Synchronized EEG and Telemetry for Traffic Research |
| title_short | Development of a Networked Multi-Participant Driving Simulator with Synchronized EEG and Telemetry for Traffic Research |
| title_sort | development of a networked multi participant driving simulator with synchronized eeg and telemetry for traffic research |
| topic | driver behavior modeling EEG synchronization human-in-the-loop simulation hyperscanning multi-participant driving simulation networked driving simulator |
| url | https://www.mdpi.com/2079-8954/13/7/564 |
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