The 100-Car Naturalistic Driving Study

NHTSA · 2005 · ROSA P / United States. Federal Motor Carrier Safety Administration

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Summary

This report presents a descriptive analysis of interactions between light vehicles (LVs) and heavy vehicles (HVs) from the perspective of the light vehicle driver, utilizing data from the 100-Car Naturalistic Driving Study. The research addresses the significant safety problem posed by LV-HV interactions; although large trucks represent only 4% of registered vehicles, they account for 8% of fatal crashes. Due to the substantial weight difference and longer stopping distances of trucks, LV occupants are highly vulnerable. Traditional crash databases and controlled empirical studies fail to capture the driver behaviors and performance leading up to these events. Consequently, this study aimed to gain a better understanding of LV-HV interactions, refine classification schemes, compare findings with prior heavy-vehicle instrumentation studies, and provide foundational data for developing safety countermeasures. The methodology involved 109 participants driving 100 instrumented light vehicles (80 privately owned, 20 leased) in the Washington, DC metropolitan area over 13 months. Vehicles were equipped with five video cameras capturing the forward roadway, driver’s face, right side, interior controls, and rear view, along with infrared lighting for night visibility. Critical incidents—defined as unexpected events requiring evasive action or resulting in close calls—were identified via sensor thresholds (e.g., braking >0.6 g), driver-activated buttons, or analyst review. From a total of 9,125 captured incidents, 246 involved LV-HV interactions. Analysts coded these events for incident type, primary maneuver, contributing factors, accident type, and critical reasons. The analysis revealed that LV-HV interactions constituted 2.7% of all recorded incidents. The most frequent incident type was "Late Braking for Stopped/Stopping Traffic." The study categorized events by fault: 79 incidents were attributed to the HV driver, 138 to the LV driver, and 29 were unknown. Primary maneuvers and contributing factors were detailed for each category, with specific attention paid to driver distraction. The report also compared these findings with data from two prior studies where heavy vehicles were instrumented (the Local/Short Haul and Sleeper Berth studies). This comparison allowed for a cross-validation of incident types, primary maneuvers, and contributing factors across different instrumentation perspectives, highlighting consistencies and discrepancies in how LV-HV conflicts are characterized. The significance of this work lies in its provision of detailed, naturalistic data on the moments preceding LV-HV conflicts, filling a gap left by traditional crash databases. By establishing a robust classification scheme and identifying specific contributing factors and critical reasons for incidents, the study offers essential background information for the development of targeted interventions. The findings support the creation of countermeasures designed to mitigate the high vulnerability of light vehicle occupants in interactions with heavy trucks, ultimately aiming to reduce the disproportionate number of fatalities and injuries associated with these crashes.

Key finding

Late braking for stopped or stopping traffic was the most frequent incident type among the 246 recorded light vehicle-heavy vehicle interactions.

Methodology

naturalistic

Sample size: 100

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.

• pre crash contributing factors
• incidence prevalence
• naturalistic crash near crash
• crash typology
• driver post crash behavior
• lane changing

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: crash risk outcomes, observational prevalence
• Methodological Resource: dataset resource