Literature DB >> 17558669

The influence of distraction and driving context on driver response to imperfect collision warning systems.

M N Lees1, J D Lee.   

Abstract

Automotive collision warning systems (CWS) can enhance hazard identification and management. However, false alarms (FAs), which occur as a random activation of the system not corresponding to a threat and not interpretable by the driver, and unnecessary alarms (UAs), which occur in situations judged hazardous by the algorithm but not by the driver, may limit CWS effectiveness. A driving simulator was used to investigate the influence of CWS (accurate, UA, FA, none) and distraction on driver performance during non-critical and critical events. FAs and UAs differentially influenced trust and compliance. FAs diminished trust and compliance, whereas the context associated with UAs fostered trust and compliance during subsequent events. This study suggests current warning descriptions based on signal detection theory need to be expanded to represent how different types of alarms affect drivers.

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Mesh:

Year:  2007        PMID: 17558669     DOI: 10.1080/00140130701318749

Source DB:  PubMed          Journal:  Ergonomics        ISSN: 0014-0139            Impact factor:   2.778


  8 in total

1.  Understanding reliance on automation: effects of error type, error distribution, age and experience.

Authors:  Julian Sanchez; Wendy A Rogers; Arthur D Fisk; Ericka Rovira
Journal:  Theor Issues Ergon Sci       Date:  2014-03

2.  Augmented reality cues to assist older drivers with gap estimation for left-turns.

Authors:  Michelle L Rusch; Mark C Schall; John D Lee; Jeffrey D Dawson; Matthew Rizzo
Journal:  Accid Anal Prev       Date:  2014-06-18

3.  Cross-modal warnings for orienting attention in older drivers with and without attention impairments.

Authors:  Monica N Lees; Joshua Cosman; John D Lee; Shaun P Vecera; Jeffrey D Dawson; Matthew Rizzo
Journal:  Appl Ergon       Date:  2011-12-26       Impact factor: 3.661

4.  Augmented reality cues and elderly driver hazard perception.

Authors:  Mark C Schall; Michelle L Rusch; John D Lee; Jeffrey D Dawson; Geb Thomas; Nazan Aksan; Matthew Rizzo
Journal:  Hum Factors       Date:  2013-06       Impact factor: 2.888

Review 5.  A selective review of simulated driving studies: Combining naturalistic and hybrid paradigms, analysis approaches, and future directions.

Authors:  V D Calhoun; G D Pearlson
Journal:  Neuroimage       Date:  2011-06-21       Impact factor: 6.556

6.  Human-Like Lane Change Decision Model for Autonomous Vehicles that Considers the Risk Perception of Drivers in Mixed Traffic.

Authors:  Chang Wang; Qinyu Sun; Zhen Li; Hongjia Zhang
Journal:  Sensors (Basel)       Date:  2020-04-16       Impact factor: 3.576

7.  Analysis of Pedestrian Street-Crossing Decision-Making Based on Vehicle Deceleration-Safety Gap.

Authors:  Hongjia Zhang; Yingshi Guo; Yunxing Chen; Qinyu Sun; Chang Wang
Journal:  Int J Environ Res Public Health       Date:  2020-12-10       Impact factor: 3.390

8.  Research on the Influence of Vehicle Speed on Safety Warning Algorithm: A Lane Change Warning System Case Study.

Authors:  Rui Fu; Yali Zhang; Chang Wang; Wei Yuan; Yingshi Guo; Yong Ma
Journal:  Sensors (Basel)       Date:  2020-05-08       Impact factor: 3.576

  8 in total

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