Literature DB >> 18215559

Validating a driving simulator using surrogate safety measures.

Xuedong Yan1, Mohamed Abdel-Aty, Essam Radwan, Xuesong Wang, Praveen Chilakapati.   

Abstract

Traffic crash statistics and previous research have shown an increased risk of traffic crashes at signalized intersections. How to diagnose safety problems and develop effective countermeasures to reduce crash rate at intersections is a key task for traffic engineers and researchers. This study aims at investigating whether the driving simulator can be used as a valid tool to assess traffic safety at signalized intersections. In support of the research objective, this simulator validity study was conducted from two perspectives, a traffic parameter (speed) and a safety parameter (crash history). A signalized intersection with as many important features (including roadway geometries, traffic control devices, intersection surroundings, and buildings) was replicated into a high-fidelity driving simulator. A driving simulator experiment with eight scenarios at the intersection were conducted to determine if the subjects' speed behavior and traffic risk patterns in the driving simulator were similar to what were found at the real intersection. The experiment results showed that speed data observed from the field and in the simulator experiment both follow normal distributions and have equal means for each intersection approach, which validated the driving simulator in absolute terms. Furthermore, this study used an innovative approach of using surrogate safety measures from the simulator to contrast with the crash analysis for the field data. The simulator experiment results indicated that compared to the right-turn lane with the low rear-end crash history record (2 crashes), subjects showed a series of more risky behaviors at the right-turn lane with the high rear-end crash history record (16 crashes), including higher deceleration rate (1.80+/-1.20 m/s(2) versus 0.80+/-0.65 m/s(2)), higher non-stop right-turn rate on red (81.67% versus 57.63%), higher right-turn speed as stop line (18.38+/-8.90 km/h versus 14.68+/-6.04 km/h), shorter following distance (30.19+/-13.43 m versus 35.58+/-13.41 m), and higher rear-end probability (9/59=0.153 versus 2/60=0.033). Therefore, the relative validity of driving simulator was well established for the traffic safety studies at signalized intersections.

Mesh:

Year:  2007        PMID: 18215559     DOI: 10.1016/j.aap.2007.06.007

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


  12 in total

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Authors:  Arthur A Simen; Cynthia Gargano; Jang-Ho Cha; Melissa Drexel; An Bautmans; Ingeborg Heirman; Tine Laethem; Thomas Hochadel; Lien Gheyle; Kim Bleys; Chan Beals; Aubrey Stoch; Gary G Kay; Arie Struyk
Journal:  Ther Adv Drug Saf       Date:  2015-06

2.  Driving simulator performance remains impaired in patients with severe OSA after CPAP treatment.

Authors:  Andrew Vakulin; Stuart D Baulk; Peter G Catcheside; Nick A Antic; Cameron J van den Heuvel; Jillian Dorrian; R Doug McEvoy
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3.  Older drivers and rapid deceleration events: Salisbury Eye Evaluation Driving Study.

Authors:  Lisa Keay; Beatriz Munoz; Donald D Duncan; Daniel Hahn; Kevin Baldwin; Kathleen A Turano; Cynthia A Munro; Karen Bandeen-Roche; Sheila K West
Journal:  Accid Anal Prev       Date:  2012-06-27

4.  Effects of simulated mild vision loss on gaze, driving and interaction behaviors in pedestrian crossing situations.

Authors:  Christian Lehsing; Florian Ruch; Felix M Kölsch; Georg N Dyszak; Christian Haag; Ilja T Feldstein; Steven W Savage; Alex R Bowers
Journal:  Accid Anal Prev       Date:  2019-02-10

5.  Driving simulation as a performance-based test of visual impairment in glaucoma.

Authors:  Felipe A Medeiros; Robert N Weinreb; Erwin R Boer; Peter N Rosen
Journal:  J Glaucoma       Date:  2012 Apr-May       Impact factor: 2.503

6.  Visuospatial and Attentional Abilities Predict Driving Simulator Performance Among Older HIV-infected Adults.

Authors:  J M Foley; A L Gooding; A D Thames; M L Ettenhofer; M S Kim; S A Castellon; T D Marcotte; J R Sadek; R K Heaton; W G van Gorp; C H Hinkin
Journal:  Am J Alzheimers Dis Other Demen       Date:  2013-01-11       Impact factor: 2.035

7.  Cognitive Load Measurement in a Virtual Reality-based Driving System for Autism Intervention.

Authors:  Lian Zhang; Joshua Wade; Dayi Bian; Jing Fan; Amy Swanson; Amy Weitlauf; Zachary Warren; Nilanjan Sarkar
Journal:  IEEE Trans Affect Comput       Date:  2017-05-23       Impact factor: 10.506

8.  Correspondence between Simulator and On-Road Drive Performance: Implications for Assessment of Driving Safety.

Authors:  Nazan Aksan; Sarah D Hacker; Lauren Sager; Jeffrey Dawson; Steven Anderson; Matthew Rizzo
Journal:  Geriatrics (Basel)       Date:  2016-03-10

9.  Classification of Fatigued and Drunk Driving Based on Decision Tree Methods: A Simulator Study.

Authors:  Ying Yao; Xiaohua Zhao; Hongji Du; Yunlong Zhang; Guohui Zhang; Jian Rong
Journal:  Int J Environ Res Public Health       Date:  2019-05-31       Impact factor: 3.390

10.  Can a novel web-based computer test predict poor simulated driving performance? a pilot study with healthy and cognitive-impaired participants.

Authors:  Tobias Nef; René M Müri; Rahel Bieri; Michael Jäger; Nora Bethencourt; Ioannis Tarnanas; Urs P Mosimann
Journal:  J Med Internet Res       Date:  2013-10-21       Impact factor: 5.428

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