A multi-method approach to understanding drivers' experiences and behavior under partial vehicle automation

Strayer, DL; Cooper, JM; Sanbonmatsu, DM; McDonnell, AS · 2023 · publications_jsonl

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Summary

This study addresses the lack of standardized metrics for measuring cognitive distraction in driving, a significant contributor to roadway injuries and fatalities. While visual and manual distractions have established evaluation guidelines, cognitive distraction—defined as the diversion of attention from driving to secondary tasks—remains difficult to assess and lacks a comprehensive framework for comparing the impairment levels of different in-vehicle activities. The research aimed to establish a systematic method for quantifying cognitive workload across common secondary tasks to determine their relative impact on driving safety. The researchers conducted three experiments involving participants performing eight distinct in-vehicle tasks: baseline single-task driving, listening to the radio, listening to an audiobook, conversing with a passenger, using a handheld cell phone, using a hands-free cell phone, interacting with a speech-to-text email system, and performing a demanding auditory Operation Span (OSPAN) math/memory task. Experiment 1 served as a laboratory baseline without driving. Experiment 2 utilized a high-fidelity driving simulator, and Experiment 3 involved driving an instrumented vehicle in a residential area. Data collection integrated primary driving performance measures (brake reaction time, following distance), secondary task performance (Detection Response Task reaction time and accuracy), subjective workload ratings (NASA-TLX), and physiological measures (EEG event-related potentials, specifically P300 latency and amplitude). Results indicated that cognitive workload increased systematically across the tasks. Listening to the radio or audiobook was associated with low cognitive workload and negligible impairment. Conversations with passengers or via cell phones (handheld or hands-free) resulted in moderate cognitive workload, characterized by increased brake reaction times and reduced following distances in the simulator. The speech-to-text email system and the OSPAN task induced high cognitive workload, significantly degrading driving performance and increasing physiological indicators of mental demand. Notably, even hands-free cell phone use caused significant cognitive distraction, demonstrating that manual and visual demands are not the sole sources of impairment. The study successfully integrated these diverse measures into a unified cognitive distraction scale, anchoring single-task driving as the lowest risk and the OSPAN task as the highest. The findings conclude that driving impairments are directly related to the cognitive workload of concurrent activities, challenging the assumption that hands-free devices are safe alternatives. The adoption of voice-based systems may have unintended adverse effects on traffic safety due to high cognitive demands. This research provides a scientifically based framework for evaluating cognitive distraction, offering policymakers and engineers a metric to compare the safety risks of various in-vehicle technologies and inform regulations regarding driver distraction.

Key finding

Drivers paid more attention to the driving environment under Level 2 partial automation than during manual driving in the initial session, but after 6-8 weeks of familiarization showed a significant decrease in attention under automation in the simpler highway environment; spectral EEG (frontal theta, parietal alpha) did not show evidence of decreased workload or engagement under automation, highlighting the importance of multiple measures and varied roadway conditions. Naturalistic data showed automation use >70% of the time, increasing system warnings with experience (more relaxed monitoring strategy), reduced automation use under higher driving demands, no automation effect on fatigue or fidgeting, and growing secondary task engagement over time. Surveys showed automation improved the driving experience, reduced stress, and increased intentions to use and purchase automated vehicles, while drivers remained cognizant of risks.

Methodology

on_road

Sample size: N=30 (12 female, 18 male; ages 18-55, M=35.7, SD=9.3); all Level 2 naive at enrollment

Provenance

The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via tag_papers on 2026-05-30 (2 acquisition events logged).

Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• automation
• automation surprise
• trust calibration
• automation complacency bias
• situational awareness
• acceptance adoption

Information type

What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).

• Empirical Findings: behavioral performance data, observational prevalence
• Theoretical Contribution: conceptual framework