Literature DB >> 29277384

Effectiveness of visual warnings on young drivers hazard anticipation and hazard mitigation abilities.

Foroogh Hajiseyedjavadi1, Tingru Zhang2, Ravi Agrawal3, Michael Knodler4, Donald Fisher5, Siby Samuel6.   

Abstract

Previous studies have demonstrated that young drivers fail both to scan for and mitigate latent hazards mostly due to their cluelessness. This study aims to investigate whether these skills could be improved by providing young drivers with alerts in advance of the upcoming threat using a driving simulator experiment. In particular, the warning was presented on the head-up displays (HUD) either 2 s, 3 s or 4 s in advance of a latent threat. The hazard anticipation, hazard mitigation and attention maintenance performance of forty-eight young drivers aged 18-25 was evaluated across eight unique scenarios either in the presence or in the absence of latent threat alerts displayed on a HUD. There were four groups overall: one control group (no alert) and three experimental groups (2 s alert, 3 s alert and 4 s alert). The analysis of the hazard anticipation data showed that all three experimental groups with HUD warnings (2 s, 3 s, 4 s) significantly increased the likelihood that drivers would glance towards latent pedestrian and vehicle hazards when compared to the control group. The hazard mitigation analysis showed that in situations involving a pedestrian threat, HUD alerts provided 3 or 4 s in advance of a potential threat led drivers to travel significantly slower than the control group or the 2 s group. No significant effect of a HUD alert on drivers' speed was found when the latent hazard was a vehicle. An analysis of eye behaviors showed that only 7 out of 597 glances at the HUD were longer than 2 s safety-threshold, indicating that the warnings do not seem to distract the driver.
Copyright © 2017. Published by Elsevier Ltd.

Keywords:  Driving simulator; Hazard anticipation; Hazard mitigation; Head-up display; Visual collision warning; Young drivers

Mesh:

Year:  2017        PMID: 29277384     DOI: 10.1016/j.aap.2017.11.037

Source DB:  PubMed          Journal:  Accid Anal Prev        ISSN: 0001-4575


  1 in total

1.  Intelligent Driving Assistant Based on Road Accident Risk Map Analysis and Vehicle Telemetry.

Authors:  José Terán; Loraine Navarro; Christian G Quintero M; Mauricio Pardo
Journal:  Sensors (Basel)       Date:  2020-03-22       Impact factor: 3.576

  1 in total

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