Dashcam video footage-based analysis of microsleep-related behaviors in truck collisions attributed to falling asleep at the wheel

Hajime Kumagai; Kengo Kawaguchi; Hiroyuki Sawatari; Yuka Kiyohara; Mitsuo Hayashi; Toshiaki Shiomi

doi:10.1016/j.aap.2023.107070

Dashcam video footage-based analysis of microsleep-related behaviors in truck collisions attributed to falling asleep at the wheel

Accid Anal Prev. 2023 Jul:187:107070. doi: 10.1016/j.aap.2023.107070. Epub 2023 Apr 13.

Authors

Hajime Kumagai¹, Kengo Kawaguchi², Hiroyuki Sawatari³, Yuka Kiyohara², Mitsuo Hayashi⁴, Toshiaki Shiomi²

Affiliations

¹ Department of Sleep Medicine, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima 7348553, Japan. Electronic address: kumaguy88@hiroshima-u.ac.jp.
² Department of Sleep Medicine, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima 7348553, Japan.
³ Department of Perioperative and Critical Care Management, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima 7348553, Japan.
⁴ Graduate School of Integrated Arts and Sciences, Hiroshima University, 1-7-1 Kagamiyama, Higashi-Hiroshima 7398521, Japan.

PMID: 37060664
DOI: 10.1016/j.aap.2023.107070

Abstract

Objective: With the rapid spread of dashcams, many car accidents have been recorded; however, behavioral approaches using these dashcam video footage have not been sufficiently examined. We employed dashcam video footage to evaluate microsleep-related behaviors immediately prior to real-world truck collisions in professional drivers to explore a new solution to reduce collisions attributed to falling asleep at the wheel.

Methods: In total, 3,120 s of video footage (60 s/case × 52 cases) from real-world truck collisions of 52 professional drivers obtained from interior and exterior dashcams were used and visually analyzed in a second-by-second manner to simultaneously evaluate any eye changes and microsleep-related behaviors (the driver's anti-sleepiness behavior, behavioral signs of microsleep, and abnormal vehicle behavior) during driving.

Results: Assessment of the frequency of occurrence of each item of microsleep-related behavior in the 52 collisions revealed that the item "touching" in terms of anti-sleepiness behavior, "absence of body movement" in terms of behavioral signs of microsleep, and "inappropriate line crossing" in terms of abnormal vehicle behavior were observed at the highest rate in all drivers (46.2%, 75.0%, and 78.8%, respectively). Decreases in anti-sleepiness behavior coincided with increases in behavioral signs of microsleep and abnormal vehicle behavior, with collisions occurring within approximately 40 s of these changes. Collisions were more common among young people and in the early morning and evening.

Conclusion: Our dashcam video footage-based analysis in truck collisions attributed to falling asleep at the wheel revealed the process of changes in microsleep-related driver and vehicle behaviors, classified as anti-sleepiness behavior, behavioral signs of microsleep, and abnormal vehicle behavior. Based on these findings, to prevent collisions caused by falling asleep at the wheel, it is crucial to monitor not only the driver's eyes, but also the driver's whole body and vehicle behavior simultaneously to reliably detect microsleep-related behaviors.

Keywords: Car accident; Dashcam; Driver behavior; Microsleep; Truck collision; Vehicle behavior.

MeSH terms

Accidents, Traffic* / prevention & control
Adolescent
Automobile Driving*
Humans
Motor Vehicles