Deskilling or Redefining? Drivers’ Hazard Anticipation with Automation
Hazard anticipation is a critical skill for crash avoidance, requiring drivers to detect, predict, and respond to potential roadway threats. As Advanced Driver Assistance Systems (ADAS) become more common, drivers increasingly shift from active vehicle control to supervisory roles, which may change visual scanning patterns, situational awareness, and response strategies. These changes are not yet fully understood and may have important safety implications. This project investigates how automation affects hazard anticipation by examining driver gaze behavior, responses to traditional roadway hazards, and reactions to system-related cues in ADAS-equipped vehicles. The research integrates secondary data analysis, development of a hazard anticipation taxonomy, and experimental testing using a high-fidelity driving simulator. Results will be used to identify automation-related changes in hazard anticipation and to develop targeted training approaches aimed at maintaining effective driver oversight. Findings from this work will support safer human interaction with automated vehicle technologies as their use continues to expand.
- Record URL:
Language
- English
Project
- Status: Active
- Funding: $140,000.00
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Contract Numbers:
69A3552348301
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Sponsor Organizations:
Office of the Assistant Secretary for Research and Technology
University Transportation Centers Program
Department of Transportation
Washington, DC United States 20590 -
Managing Organizations:
University of Massachusetts, Amherst
Department of Civil and Environmental Engineering
130 Natural Resources Road
Amherst, MA United States 01003 -
Performing Organizations:
University of Massachusetts, Amherst
Department of Civil and Environmental Engineering
130 Natural Resources Road
Amherst, MA United States 01003 -
Principal Investigators:
Pradhan, Anuj
- Start Date: 20260101
- Expected Completion Date: 20261231
- Actual Completion Date: 0
- USDOT Program: University Transportation Centers Program
- Subprogram: University Transportation Centers
Subject/Index Terms
- TRT Terms: Data analysis; Driver support systems; Driving behavior; Driving simulators; Eye movements; Human machine systems; Risk assessment
- Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Safety and Human Factors;
Filing Info
- Accession Number: 01974411
- Record Type: Research project
- Source Agency: New England University Transportation Center
- Contract Numbers: 69A3552348301
- Files: UTC, RIP
- Created Date: Dec 18 2025 2:13PM