Comparative assessment of crash causal factors and IVHS countermeasures

Najm, W. C.; Koziol, J. S.; Tijerina, L.; Pierowicz, J. A.; Hendricks, Donald L. · 1994 · ROSA P / John A. Volpe National Transportation Systems Center (U.S.)

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Summary

This paper outlines a methodology developed by the Volpe National Transportation Systems Center and NHTSA to analyze crash causal factors and identify applicable Intelligent Vehicle-Highway System (IVHS) countermeasures. The research aims to define crash avoidance opportunities to guide the development of IVHS technologies. The study focuses on five primary crash types: rear-end, backing, single vehicle roadway departure (SVRD), lane change, and intersection/crossing path (I/CP) crashes. The methodology involves quantifying baseline crash sizes using General Estimates System (GES) and Fatal Accident Reporting System (FARS) data, identifying contributing circumstances through case file assessments, devising functional countermeasure concepts, and deriving kinematic models to estimate the time and intensity required for evasive actions. The paper illustrates this methodology through detailed analyses of lane change and I/CP crashes. For lane change crashes, which accounted for approximately 4% of police-reported crashes in 1991, the primary causal factors were identified as failing to see the other vehicle (61.2%) and misjudging gap/velocity (29.9%). The analysis distinguished between "proximity" crashes, involving minimal longitudinal gaps, and "fast approach" crashes, involving significant gaps closed at high velocity differentials. Proposed countermeasures included presence indicators to prevent hazardous maneuvers, driver warning systems for imminent collisions, and control-intervention systems such as variable resistance steering or soft braking. Kinematic modeling was used to determine the available time for driver response, highlighting the need for systems that account for driver reaction delays and vehicle performance lags. For I/CP crashes, the study analyzed four subtypes: signalized and unsignalized straight crossing paths, and left-turn across path crashes. Causal factors varied by subtype; driver inattention to signals or signs was dominant in straight crossing path crashes, while obstructed vision was more prevalent in left-turn crashes. Countermeasures were categorized by operational complexity. First-level countermeasures for recognition errors included in-vehicle signing and proximity traffic displays. For decision errors, such as misjudging gaps or attempting to beat amber lights, warning systems with decision-making capabilities were proposed. The study also identified crash-type-independent countermeasures for factors like driver physiological impairment, vehicle defects, and adverse road conditions, such as impaired driver monitors and pavement condition sensors. The synthesis of causal factors across the five crash types revealed that driver recognition errors were the leading cause, followed by decision errors and physiological impairment. Driver recognition errors, including inattention and obstructed vision, were amenable to first-level countermeasures like situation displays and headway detection systems. Decision errors, such as tailgating or excessive speed, required warning systems with decision-making capabilities. Erratic actions and unlawful driving were deemed suitable only for fully automatic control systems. The study concludes that while specific countermeasure effectiveness estimates could not be derived for all crash types due to data limitations, the established methodology provides a framework for defining functional requirements and identifying research needs for IVHS crash avoidance systems.

Key finding

Driver recognition errors were the leading cause of the analyzed target crashes, followed by driver decision errors and physiological impairment.

Methodology

dataset

Sample size: 682

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
• crash typology
• naturalistic crash near crash
• causation analyses
• incidence prevalence
• 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).

• Applied Guidance: countermeasure evaluation
• Empirical Findings: crash risk outcomes
• Methodological Resource: dataset resource