Evaluating User Acceptance and Effectiveness of Cognitive Measurements and Intervention for Shared Autonomy
Vehicles equipped with automated driving systems (ADS) have become more widespread in the trucking industry. On the one hand, ADS are known to be susceptible to occasional errors in environment perception, but on the other, ADS can demonstrate safer and more efficient behavior in situations where the driver is cognitively impaired. Shared autonomy systems thus have the potential to combine the best of both paradigms. Some early instantiations of such shared autonomy ADS use measurements of the human cognitive state to perform interventions, either in the form of sensory feedback, and/or by actively taking over the driving task. The main objective of this project is to address the gap in research on the effectiveness and acceptance of cognition-aware shared-autonomy methods with respect to the overall system safety. Qualitative data will be collected through semi-structured interviews with truck drivers and systematically encoded into operational design requirements and hypothesis-driven performance metrics that directly inform the design of cognition-aware shared autonomy systems. The research team will perform a driving simulator study that enables a controlled evaluation of adaptive cognition-aware intervention policies, including rule-based and data-driven triggering mechanisms that dynamically adjust system behavior based on real-time cognitive interventions. Researchers will study how specific design choices in cognition-aware intervention policies (e.g., trigger thresholds, modality selection, and intervention persistence) influence system acceptance, misuse, and compliance, enabling actionable design guidance beyond descriptive acceptance analysis. The datasets collected inform policy on the use of ADS in both drayage and long-haul trucking. This project will develop a methodology for designing and evaluating cognition-aware behavioral interventions that couple driver monitoring outputs with explicit control and feedback policies, enabling reproducible comparison across intervention strategies and deployment contexts.
Language
- English
Project
- Status: Active
- Funding: $210,000.00
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Contract Numbers:
DOT 69A3552348319
DOT 69A3552344814
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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 20590National Center for Sustainable Transportation
University of California, Davis
Davis, CA United StatesUniversity of California, Davis
1 Shields Ave
Davis, California United States 95616 -
Managing Organizations:
National Center for Sustainable Transportation
University of California, Davis
Davis, CA United StatesUniversity of California, Davis
1 Shields Ave
Davis, California United States 95616 -
Project Managers:
Cliff, Sydney
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Performing Organizations:
National Center for Sustainable Transportation
University of California, Davis
Davis, CA United StatesUniversity of California, Davis
1 Shields Ave
Davis, California United States 95616University of Southern California, Los Angeles
University Park Campus
Los Angeles, CA United States 90089 -
Principal Investigators:
Nordhoff, Sina
Deshmukh, Jyotirmoy
- Start Date: 20260401
- Expected Completion Date: 20270331
- Actual Completion Date: 0
- USDOT Program: University Transportation Centers
Subject/Index Terms
- TRT Terms: Automated vehicle control; Cognition; Cognitive impairment; Data collection; Driver monitoring; Driving simulators; Truck drivers
- Subject Areas: Data and Information Technology; Highways; Safety and Human Factors; Vehicles and Equipment;
Filing Info
- Accession Number: 01985425
- Record Type: Research project
- Source Agency: National Center for Sustainable Transportation
- Contract Numbers: DOT 69A3552348319, DOT 69A3552344814
- Files: UTC, RIP
- Created Date: Apr 9 2026 2:23PM