IVHS Countermeasures for Rear-End Collision, Task 1 Vol. VI: Human Factors Studies

Wilson, Terry · 1994 · ROSA P / United States. Department of Transportation. National Highway Traffic Safety Administration

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Summary

This interim report, Volume VI of the NHTSA-sponsored "IVHS Countermeasures for Rear-End Collisions" program, addresses the human factors contributing to rear-end collisions to establish a foundation for developing performance specifications for collision avoidance systems. The research was motivated by the prevalence of rear-end crashes, which constitute approximately 23.8% of all motor vehicle crashes, and the need to design autonomous in-vehicle technologies that effectively augment driver sensory and cognitive capabilities. The primary goal was to identify causal factors, quantify crash dynamics, and define the human performance requirements necessary for effective system intervention. The study employed a comprehensive literature review and clinical analysis of existing accident databases, including NHTSA’s Fatal Accident Reporting System (FARS), General Estimates System (GES), and National Accident Sampling System (NASS) Crashworthiness Data System (CDS) from 1985, 1991, and 1992. The authors decomposed rear-end crashes into two categories: lead-vehicle stationary (LVS) and lead-vehicle moving (LVM). They analyzed driver behavior, perceptual cues, and reaction times to develop an expanded model of the rear-end crash timeline. This model integrates epidemiological data with empirical research to predict crash scenarios and evaluate the potential effectiveness of various intervention systems, categorized as driver action, headway maintenance, or automatic control systems. Key findings indicate that driver inattention is the dominant causal factor, accounting for 63% of sampled rear-end collisions, while following too closely contributes to 14%. The analysis revealed that most collisions occur in "close-following" situations where the following vehicle travels at a constant velocity and the lead vehicle decelerates. The report details how in-vehicle tasks divert visual attention, noting that glance durations often exceed 1.2 seconds, creating periods where drivers fail to process forward roadway information. Perceptual factors, such as the reliance on relative speed cues and visual angle changes, were found to influence drivers' comfort with close following distances. Additionally, the study reviewed perception-reaction times (PRT), noting that unexpected events require longer reaction times (up to 1.6 seconds for the 95th percentile) compared to expected stimuli. The authors also examined display modalities, highlighting the importance of managing false alarms and selecting appropriate auditory, visual, or tactile warnings. The significance of this work lies in its provision of a structured framework for designing rear-end collision avoidance systems. By quantifying human limitations and crash dynamics, the report establishes preliminary performance guidelines that ensure future technologies align with driver capabilities. The developed model serves as a tool for sensitivity analyses and case studies to determine whether specific systems could mitigate crashes. This foundation supports subsequent phases of the project, including the testing of existing technologies and the refinement of final performance specifications, ultimately aiming to reduce the frequency and severity of rear-end collisions through effective human-machine integration.

Key finding

Driver inattention accounts for 63% of rear-end collisions, with the most common scenario involving a following vehicle traveling at constant velocity while the lead vehicle decelerates to a stop.

Methodology

review

Provenance

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

Topics

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• time to collision looming
• following distance
• braking response
• perception reaction time
• crash reconstruction hf
• behavioral adaptation risk compensation

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, crash risk outcomes, observational prevalence