From Perception to Policy: Measuring Road Driver Behavior and Public Support for Autonomous Truck Regulation

Term Start:

January 1, 2026

Term End:

December 31, 2026

Budget:

$108,468

Keywords:

Autonomous Trucks, Driver Behavior, Emerging Technologies, Safety, Transportation Infrastructure

Thrust Area(s):

Understanding User Needs

University Lead:

Georgia Institute of Technology

Researcher(s):

Sofia Perez-Guzman; Mengyao Li

This project will investigate how human drivers respond when sharing the road with autonomous trucks, generating behavioral evidence to guide public policy, risk communication, and regulatory design for the safe and economically beneficial deployment of freight automation. Truck automation is projected to deliver major efficiency and safety gains to the United States economy by addressing driver shortages, improving fuel efficiency, and reducing crash risk on long-haul corridors. Yet, public acceptance and regulatory readiness remain key barriers to realizing these benefits. To address this gap, the study will move beyond stated attitudes to examine observed behavior using the construct of existence acceptance, i.e., the degree to which non-users feel comfortable with a technology operating in their environment. Four determinants of acceptance will be explored: prior experience, perceived benefits, perceived threats, and affective responses. The study will employ a controlled driving simulation in which licensed participants encounter scripted highway scenarios involving overtaking and merging maneuvers with both autonomous and human-driven trucks, as well as passenger cars. Experimental conditions will vary the informational framing of autonomous trucks, i.e., from neutral descriptions to messages emphasizing regulatory safeguards and societal benefits, to test how communication context shapes trust, compliance, and comfort. During the simulation, vehicle kinematics (speed, headway, and lane position), gaze tracking, and physiological responses (heart rate and electrodermal activity) will be recorded. These measures will be paired with pre- and post-drive surveys assessing perceived safety, trust, and support for regulatory oversight. Data analysis will integrate behavioral and psychophysiological modeling to quantify the effects of informational framing and exposure on driver adaptation and perceptions of safety. The findings will directly inform safe automation and freight efficiency, helping decision-makers balance innovation with public trust and position autonomous trucking as a driver of economic productivity and safety in the national freight system.

Project scope

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