European NCAP Driver State Monitoring Protocols: Prevalence of Distraction in Naturalistic Driving

Abstract

Objective

examine the prevalence of driver distraction in naturalistic driving when implementing European New Car Assessment Program (Euro NCAP)-defined distraction behaviours.

Background

The 2023 introduction of Occupant Status monitoring (OSM) into Euro NCAP will accelerate uptake of Driver State Monitoring (DSM). Euro NCAP outlines distraction behaviours that DSM must detect to earn maximum safety points. Distraction behaviour prevalence and driver alerting and intervention frequency have yet to be examined in naturalistic driving.

Method

Twenty healthcare workers were provided with an instrumented vehicle for approximately two weeks. Data were continuously monitored with automotive grade DSM during daily work commutes, resulting in 168.8 hours of driver head, eye and gaze tracking.

Results

Single long distraction events were the most prevalent, with .89 events/hour. Implementing different thresholds for driving-related and driving-unrelated glance regions impacts alerting rates. Lizard glances (primarily gaze movement) occurred more frequently than owl glances (primarily head movement). Visual time-sharing events occurred at a rate of .21 events/hour.

Conclusion

Euro NCAP-described driver distraction occurs naturalistically. Lizard glances, requiring gaze tracking, occurred in high frequency relative to owl glances, which only require head tracking, indicating that less sophisticated DSM will miss a substantial amount of distraction events.

Application

This work informs OEMs, DSM manufacturers and regulators of the expected alerting rate of Euro NCAP defined distraction behaviours. Alerting rates will vary with protocol implementation, technology capability, and HMI strategies adopted by the OEMs, in turn impacting safety outcomes, user experience and acceptance of DSM technology.

Keywords

Euro NCAP distraction driver state monitoring visual time sharing naturalistic driving

Get full access to this article

View all access options for this article.

References

Anderson

Horne

J. A.

(2013). Driving drowsy also worsens driver distraction. Sleep Medicine, 14(5), 466–468. https://doi.org/10.1016/j.sleep.2012.11.014

Dingus

T. A.

Guo

Lee

Antin

J. F.

Perez

Buchanan-King

Hankey

(2016). Driver crash risk factors and prevalence evaluation using naturalistic driving data. Proceedings of the National Academy of Sciences, 201513271. https://doi.org/10.1073/pnas.1513271113

Donmez

Boyle

L. N.

Lee

J. D.

(2007). Safety implications of providing real-time feedback to distracted drivers. Accident Analysis & Prevention, 39(3), 581–590. https://doi.org/10.1016/j.aap.2006.10.003

Euro NCAP 2025 Roadmap- In pursuit of Vision Zero

. (2022). European new car assessment programme (Euro NCAP). https://cdn.euroncap.com/media/70315/euro-ncap-assessment-protocol-sa-safe-driving-v101.pdf

Faulks

I. J.

(2020). Caught red-handed: Automatic cameras will spot mobile-using motorists, but at what cost? The Conversation. https://theconversation.com/caught-red-handed-automatic-cameras-will-spot-mobile-using-motorists-but-at-what-cost-125638

Fitzharris

Lenné

M. G.

Corben

Arundell

T. P.

Peiris

Liu

Stephens

Fitzgerald

Judson

Bowman

Gabler

Morris

Tingvall

(2020). ECIS report 1. Overview and analysis of crash type, injury outcomes and contributing factors. (No. 343; p. 232). Monash University Accident Research Centre.

Fitzharris

Liu

Stephens

A. N.

Lenné

M. G.

(2017). The relative importance of real-time in-cab and external feedback in managing fatigue in real-world commercial transport operations. Traffic Injury Prevention, 18(sup1), S71–S78. https://doi.org/10.1080/15389588.2017.1306855

Fredriksson

Lenné

M. G.

van Montfort

Grover

(2021). European NCAP program developments to address driver distraction, drowsiness and sudden sickness. Frontiers in Neuroergonomics, 2, 786674. https://doi.org/10.3389/fnrgo.2021.786674

Fridman

J. L.

Reimer

Victor

(2016). ‘Owl’ and ‘Lizard’: Patterns of head pose and eye pose in driver gaze classification. IET Computer Vision, 10(4), 308–313. https://doi.org/10.1049/iet-cvi.2015.0296

10.

Klauer

S. G.

Dingus

T. A.

Neale

V. L.

Sudweeks

J. D.

Ramsey

D. J.

(2006). The impact of driver inattention on near-crash/crash risk: An analysis using the 100-car naturalistic driving study data (Technical Report DOT-HS-810594). National Highway Traffic Safety Administration. https://www.nhtsa.gov/DOT/NHTSA/NRD/Multimedia/PDFs/CrashAvoidance/2006/DriverInattention.pdf

11.

Kuo

Lenné

M. G.

Mulhall

Sletten

Anderson

Howard

Rajaratnam

Magee

Collins

(2018). Continuous monitoring of visual distraction and drowsiness in shift-workers during naturalistic driving. Safety Science. https://doi.org/10.1016/j.ssci.2018.11.007

12.

Liang

Lee

J. D.

Horrey

W. J.

(2014). A looming crisis: The distribution of off-road glance duration in moments leading up to crashes/near-crashes in naturalistic driving. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 58(1), 2102–2106. https://doi.org/10.1177/1541931214581442

13.

Mulhall

M. D.

Cori

Sletten

T. L.

Kuo

Lenné

M. G.

Magee

Spina

M.-A.

Collins

Anderson

Rajaratnam

S. M. W.

Howard

M. E.

(2020). A pre-drive ocular assessment predicts alertness and driving impairment: A naturalistic driving study in shift workers. Accident Analysis & Prevention, 135, 105386. https://doi.org/10.1016/j.aap.2019.105386

14.

Oviedo-Trespalacios

Briant

Kaye

S.-A.

King

(2020). Assessing driver acceptance of technology that reduces mobile phone use while driving: The case of mobile phone applications. Accident Analysis & Prevention, 135, 105348. https://doi.org/10.1016/j.aap.2019.105348

15.

Reagan

I. J.

Cicchino

J. B.

Kerfoot

L. B.

Weast

R. A.

(2018). Crash avoidance and driver assistance technologies – Are they used? Transportation Research Part F: Traffic Psychology and Behaviour, 52, 176–190. https://doi.org/10.1016/j.trf.2017.11.015

16.

Reinmueller

Koehler

Steinhauser

(2018). Adaptive warning signals adjusted to driver passenger conversation: Impact of system awareness on behavioral adaptations. Transportation Research Part F: Traffic Psychology and Behaviour, 58, 242–252. https://doi.org/10.1016/j.trf.2018.06.013

17.

Reinmueller

Steinhauser

(2019). Adaptive forward collision warnings: The impact of imperfect technology on behavioral adaptation, warning effectiveness and acceptance. Accident Analysis & Prevention, 128, 217–229. https://doi.org/10.1016/j.aap.2019.04.012

18.

Roady

Wilson

K. M.

Kuo

Lenne

M. G.

(2020). How do drivers hold their phone? Age, prevalence, & handedness. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 64. https://doi.org/10.1177/1071181320641298

19.

Sundfør

H. B.

Sagberg

Høye

(2019). Inattention and distraction in fatal road crashes – Results from in-depth crash investigations in Norway. Accident Analysis & Prevention, 125, 152–157. https://doi.org/10.1016/j.aap.2019.02.004

20.

Tivesten

Victor

T. W.

Gustavsson

Johansson

Ljung Aust

(2019). Out-of-the-loop crash prediction: The automation expectation mismatch (AEM) algorithm. IET Intelligent Transport Systems, 13(8), 1231–1240. https://doi.org/10.1049/iet-its.2018.5555

21.

Yang

Kuo

Lenné

M. G.

(2018). Analysis of gaze behavior to measure cognitive distraction in real-world driving. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 62(1), 1944–1948. https://doi.org/10.1177/1541931218621441

22.

Yang

Shiferaw

Roady

Kuo

Lenné

M. G.

(2021). Drivers glance like lizards during cell phone distraction in assisted driving. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 65(1), 1410–1414. https://doi.org/10.1177/1071181321651147

23.

Young

K. L.

Osborne

Koppel

Charlton

Grzebieta

Williamson

Haworth

Senserrick

(2019). What are Australian drivers doing behind the wheel?: An overview of secondary task data from the Australian naturalistic driving study. Journal of the Australasian College of Road Safety, 30(1), 27–33. DOI:10.1145/1620509.1620531. https://search.informit.org/doi/abs/10.3316/INFORMIT.273295494260606

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.30 MB