Influence of Driving Experience on Distraction Engagement in Automated Vehicles

He, Dengbo; Donmez, Birsen · 2019 · Transportation Research Record

DOI: 10.1177/0361198119843476

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Summary

This study investigates how driving experience influences distraction engagement in automated vehicles, addressing a gap in prior research that primarily focused on experienced drivers. The motivation stems from evidence that drivers often fail to intervene when automation fails, potentially due to increased secondary task engagement. The authors hypothesized that novice drivers, who possess less developed skills for distributing attention, might exhibit amplified distraction risks compared to experienced drivers when using automated systems. The research utilized a driving simulator study divided into two phases: Phase 1 involved manual driving, and Phase 2 involved SAE Level 2 automation (combining Adaptive Cruise Control and Lane Keeping Assist). A total of 64 participants were recruited, split evenly between novice drivers (licensed <3 years, <10,000 km driven in the past year) and experienced drivers (licensed ≥8 years, >20,000 km driven in the past year). Each phase included 32 participants, half of whom performed a self-paced visual-manual secondary task mimicking in-vehicle infotainment systems. Data collection included eye-tracking metrics (glance duration, rate, and manual interactions), physiological measures (heart rate, galvanic skin response), and self-reported workload (NASA-TLX) and perceived risk. Statistical analyses employed negative binomial regression for count data and mixed linear models for continuous variables. The results revealed significant differences in secondary task engagement based on driving experience and automation level. In manual driving, there were no significant differences between novice and experienced drivers regarding manual interaction rates with the secondary task. However, under automation, novice drivers exhibited a 58% higher manual interaction rate than experienced drivers. Furthermore, while experienced drivers generally had shorter average glance durations toward the secondary task than novices, this difference widened significantly with automation. Novice drivers also showed longer average glance durations and higher rates of long glances (>2 seconds) in the automated condition compared to manual driving. Experienced drivers maintained more conservative glance behaviors, with a 62% lower rate of long glances than novices. Regarding workload, the presence of a secondary task increased self-reported workload in manual driving but had no significant effect in automated driving, suggesting drivers perceived greater spare capacity when automation was active. No significant effects were found for physiological workload measures. The study concludes that driving experience leads to potentially safer secondary task engagement behaviors in the presence of vehicle automation. Experienced drivers appear more conservative in their visual and manual interactions with distractions compared to novices when automation is engaged. However, the authors note that most participants lacked prior experience with automated systems, suggesting that long-term exposure to automation could alter these behaviors. The findings imply that novice drivers may be at higher risk for distraction-related issues in automated vehicles, highlighting the need for further research into how automation experience and age factors influence driver behavior and crash risk.

Key finding

Novice drivers engage more frequently and for longer durations with secondary tasks in automated driving conditions compared to experienced drivers, who demonstrate more conservative distraction behaviors.

Methodology

simulator

Sample size: 64

Provenance

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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.

• temporal
• manual
• automation
• automation surprise
• novice drivers
• visual

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