The 100-Car Naturalistic Driving Study: A Descriptive Analysis of Light Vehicle-Heavy Vehicle Interactions from the Light Vehicle Driver's Perspective
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Summary
This report analyzes interactions between light vehicles (LVs) and heavy vehicles (HVs) using data from the 100-Car Naturalistic Driving Study. The research addresses the limitations of traditional crash databases and controlled empirical studies, which fail to capture the nuanced driver behaviors and performance leading up to crashes. While HVs account for a disproportionate share of fatal crashes due to their mass and stopping distances, prior naturalistic studies often lacked instrumentation in LVs. This study aims to provide a comprehensive understanding of LV-HV interactions from the LV driver’s perspective, develop a classification scheme for these events, compare findings with previous HV-instrumented studies, and establish a baseline for future countermeasure development. The methodology involved 109 participants driving 100 instrumented vehicles (80 privately owned, 20 leased) in the Washington, DC metropolitan area over 13 months. Each vehicle was equipped with five video cameras capturing the forward roadway, driver’s face, right side, interior controls, and rear view, along with sensor data. 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. Analysts coded 246 identified LV-HV interactions for incident type, primary maneuver, contributing factors, accident type, and critical reasons. The study found that LV-HV interactions constituted 2.7% of all 9,125 captured incidents. The most frequent incident type was "Late Braking for Stopped/Stopping Traffic." The analysis categorized events by fault: 138 incidents were LV-driver at-fault, 79 were HV-driver at-fault, and 29 were unknown. Driver distraction was a significant contributing factor, with specific sub-categories analyzed for each fault group. The report details the frequency and ranking of primary maneuvers, conflict types, and contributing factors, utilizing a taxonomy structure to characterize the events. Additionally, the study compares these findings with data from the Local/Short Haul and Sleeper Berth studies, which instrumented HVs, to provide a more complete picture of interaction dynamics across different vehicle perspectives. The significance of this work lies in its contribution to traffic safety research by filling the data gap regarding LV behavior during HV interactions. By providing detailed descriptive statistics on incident types, maneuvers, and contributing factors like distraction, the report offers essential background information for developing targeted interventions. The comparison with HV-instrumented studies highlights the value of naturalistic data in understanding crash causation from multiple perspectives, supporting the development of effective countermeasures to mitigate the high vulnerability of light vehicles in collisions with heavy trucks.
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
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 bulk_ingest_rosap on 2026-05-23 (6 acquisition events logged).
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | rosap | — | — | 2 | 2026-05-23 |
| archive | success | — | — | — | 1 | 2026-05-23 |
| extract | success | cached | — | — | 2 | 2026-06-10 |
| clean | success | — | — | — | 1 | 2026-06-01 |
| chunk | success | — | — | — | 1 | 2026-06-01 |
| embed | success | — | — | — | 1 | 2026-06-02 |
| enrich | success | — | — | — | 1 | 2026-05-23 |
| promote | success | — | — | — | 1 | 2026-05-23 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 3 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 19 | 2026-06-11 |
| verify | success | — | — | — | 2 | 2026-06-10 |
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
- crash typology
- naturalistic crash near crash
- looked but failed to see
- driver post crash behavior
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