Driver inattention and highway safety

Sussman, E. D.; Bishop, H.; Madnick, B.; Walter, Robert · 1985 · ROSA P / United States. Department of Transportation. National Highway Traffic Safety Administration

archive: archived pipeline: cataloged verified

Get this paper ↗ (full text — opens at the source; we link to it, we don't host it)

Summary

This paper reviews the state of research into driver attentional processes to assess the feasibility of developing in-vehicle systems for monitoring driver alertness and reducing accidents caused by inattention. Conducted by the Transportation Systems Center for the National Highway Traffic Safety Administration (NHTSA), the study addresses the finding that attentional lapses contribute to up to 90 percent of traffic accidents. The authors analyze the psychological and physiological causes of inattention, including drowsiness, physical fatigue, excess mental workload, intoxication, and simple distraction, noting that these states result in delayed, inappropriate, or absent responses to critical driving stimuli. To quantify the impact of inattention, the authors analyzed data from the 1982 National Accident Sampling System (NASS). The analysis focused on 11,868 collision accidents where vehicles were in motion and driver response was recorded. The data revealed that 37 percent of drivers involved in these accidents took no avoidance action prior to collision. Specifically, 8 percent of cases were attributed to driver inattention, 1 percent to drowsiness, and 3 percent to alcohol impairment (blood alcohol level >0.07%). The study found that failure to respond increased linearly with driver age, with older drivers less likely to attempt avoidance maneuvers. Additionally, drivers were less likely to take avoidance action between 6:00 AM and 4:00 PM compared to later hours. The paper evaluates potential indicators for detecting inattention, categorizing them into physiological measures (e.g., EEG, EKG, eye blinking) and behavioral measures (e.g., steering wheel motion, lane drift, accelerator usage). The authors conclude that single indicators are often ambiguous and unreliable. Instead, they advocate for "complex performance signatures" derived from multivariate statistical techniques or formal driver-vehicle models. The review highlights studies using discriminant function analysis to combine variables such as lateral position error, steering reversals, and accelerator activity to distinguish between alert, drowsy, or intoxicated drivers. Formal quasi-linear describing function models also show promise, as parameters like gain and remnant change predictably with attentional diversion or intoxication. The significance of this work lies in its identification of the technical pathways for practical attention monitoring systems. The authors conclude that combining indicators of the driver’s attentional state with indicators of the driving environment significantly improves monitoring accuracy. They suggest that future research should focus on refining these complex signatures and model-based approaches to develop reliable, real-time in-vehicle instrumentation capable of detecting alertness degradation before it leads to a crash.

Key finding

In 37 percent of collision accidents where avoidance maneuvers were possible, drivers took no action prior to the collision.

Methodology

dataset

Sample size: 11868

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).

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.

• vigilance
• inattentional change blindness
• drowsiness
• situational awareness
• drowsiness detection algorithms
• distraction detection algorithms

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: theory or model