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Computational Human Behavior Modeling in Remote Driving under Latency
This project examines how remote operators can safely and efficiently support highly automated trucks when on-board automation reaches its limits. The work focuses on latency, operator workload, supervisory control, and scalable interfaces for managing multiple vehicles without degrading safety.
Through empirical research studies, this project is aimed at answering the following research questions:
- How does communication latency affect remote driving performance, takeover timing, and operator behavior?
- What factors best predict remote operator workload and supervisory performance across multiple vehicles?
- How should remote assistance interfaces communicate urgency, support handoffs, and scale to fleet-level supervision?
Related journal publications
Identifying Critical Breakpoints and Latency Effects During Remote Backup Driving. Under review (IEEE Transactions on Human-Machine Systems), 2026.
Modeling Driver Adaptation Patterns During Remote Driving Under Latency. Under review (Human Factors), 2026.