Driver Distraction Analysis on Naturalistic Heavy Vehicle Data: Task 2: Analysis of Sleeper Berth Data for Distraction Events – Draft Final Report

Hanowski, Richard J.; Olson, Rebecca L; Perez, Miguel; Dingus, Thomas A. · 2001 · ROSA P / Virginia Tech Transportation Institute

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Summary

This report analyzes driver distraction events identified within the "Sleeper Berth" naturalistic driving study, sponsored by the Federal Motor Carriers Safety Administration. The primary objective was to characterize distraction-related critical incidents among long-haul truck drivers to understand their frequency, types, and associated visual and manual demands. The study utilized data from 41 drivers, focusing on 178 critical incidents attributed to driver distraction out of 2,737 total critical events recorded. The analysis aimed to develop taxonomies for distraction types, assess eye glance patterns, and compare distraction incidents against baseline driving events. The methodology involved a critical incident analysis approach using in-vehicle video and sensor data. Trained analysts reviewed video footage of triggered events, which were identified by sensors detecting maneuvers such as hard braking, rapid steering, or lane deviations. Analysts coded each incident for cause, severity, and environmental conditions. For distraction events, researchers developed a taxonomy of 36 specific distraction types, categorized into high-level task groups, detailed task groups, and driver resource groups (visual, manual, speech). An eye glance analysis was conducted for a 20-second window surrounding each incident, categorizing driver gaze into eight locations, including the forward roadway, mirrors, instrument panel, and other areas. Statistical analyses compared distraction incidents to baseline events regarding driver performance and gaze behavior. The results indicated that distraction was the third most common cause of critical incidents, accounting for 6.5% of all events, following judgment errors and other vehicle faults. Distraction incidents were disproportionately concentrated among a small subset of drivers; 33 drivers experienced distraction incidents, with two drivers accounting for 24% of all such events. Single-driver operations accounted for 65% of distraction incidents, compared to team operations. The most frequent specific distraction was looking at objects outside the vehicle, followed by CB radio use and cell phone interactions. The taxonomy revealed that many distractions involved manual demands (hands off the wheel) and visual demands (eyes off the road). Eye glance analysis showed that during distraction events, drivers spent significantly less time looking at the forward roadway and more time looking at interior cab elements or outside objects compared to baseline driving. The study also noted that exposure frequency was not fully captured, meaning high-frequency, low-risk activities might be underrepresented in the incident data relative to their actual occurrence. The significance of this research lies in its detailed characterization of distraction behaviors in naturalistic heavy vehicle settings. By establishing a comprehensive taxonomy and quantifying the visual and manual resources consumed by specific distractions, the study provides a foundation for understanding how non-driving tasks contribute to critical incidents. The finding that a small number of drivers and single-driver operations are associated with higher distraction incident rates suggests potential targets for safety interventions. Furthermore, the data highlights the prevalence of visual distractions, such as looking outside the vehicle or at interior objects, emphasizing the need for strategies that mitigate eyes-off-road behaviors in commercial trucking.

Key finding

Driver distraction accounted for 6.5% of critical incidents, with single-operation drivers experiencing 65% of these events and a small number of drivers responsible for the majority of occurrences.

Methodology

naturalistic

Sample size: 41

Provenance

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

Topics

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• visual
• temporal
• external distraction
• distraction detection algorithms
• visual manual
• manual

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