Planning and Policy for Safer Roads With Autonomous Vehicles: Decision Making Behavior in Dilemma-Inducing Situations - Phase II

Details

• Creators:
  Escalante, Norma ; Nazari, Fatemeh ; Contreras, Victor ; Noruzoliaee, Mohamadhossein
• Corporate Creators:
  University of Texas Rio Grande Valley
• Corporate Contributors:
  Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC) ; United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
• Subject/TRT Terms:
  • Autonomous vehicles
  • Transportation safety
  • Policy analysis
  • Deployment
  • Behavior
  • Transportation planning
  • Decision making
  • Surveys
  • Traffic simulation
  • Vulnerable road users
  More +
• Publication/ Report Number:
  620
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Edition:
  Final Report (July 1, 2025 - July 31, 2026)
• Corporate Publisher:
  Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC)
• Description:
  The safe deployment of autonomous vehicles (AVs) depends not only on advances in automated driving technology but also on understanding how humans respond when confronted with complex, safety-critical situations. This project investigated human decision-making and take-over behavior in AV dilemma scenarios, where automated systems and human drivers may face competing safety priorities involving vehicle occupants and vulnerable road users. To move beyond conventional hypothetical surveys and thought experiments, the study developed a multimodal neurobehavioral experimental framework using an interaction driving simulator. Neurophysiological responses, subjective emotional assessments, observed take-over behavior, and scenario characteristics were collected and integrated within a hierarchical Bayesian survival model to examine the occurrence and timing of driver intervention while accounting for individual heterogeneity. The results indicate that take-over behavior is influenced by both dilemma context and neurophysiological responses. Frontal alpha activity showed the strongest association with intervention timing, while scenarios involving multiple pedestrians were associated with a greater likelihood of intervention. Subjective emotional responses provided comparatively limited additional explanatory power, and substantial participant-specific variability was observed across the neuro-behavioral responses. These findings highlight the importance of considering both contextual and individual factors when evaluation human interaction with automated driving systems. The project provides a methodological foundation for integrating behavioral and physiological information into the evaluation of shared-control strategies, driver-monitoring systems, and other human-centered AV safety technologies.
• Format:
  PDF
• Funding:
  69A3552344811 ; 69A3552348316
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/93557/dot_93557_DS1.pdf
• File Type:
  [PDF - 10.14 MB]
• Collection(s):
  Autonomous Vehicles ; University Transportation Centers
• Main Document Checksum:
  urn:sha-512:31f3b3d789e423a325e92353d647c05d4c657c59bdb3cf9a8e66da15d8ad3941bff2b4d177c26b274c072addcfb80403bce0eb8cc3b4662d4faa136816486787