Creating Pedestrian Crash Scenarios in a Driving Simulator Environment

References

• Barrett GV. Feasibility of studying driver reaction to sudden pedestrian emergencies in an automobile simulator. Hum Factors. 1968;10:19–26.
   PubMed Web of Science ®Google Scholar
• Brewer M, Carlson P, Fitzpatrick K, Turner S, Ullman B. Improving Pedestrian Safety at Unsignalized Crossings. Washington, DC: Transportation Research Board; 2006. Transit Cooperative Research Project Report 112.
   Google Scholar
• Brown TL, He Y, Roe C, Schnell T. Is more better?—night vision enhancement system's pedestrian warning modes and older drivers. Ann Adv Automot Med. 2010;54:343–350.
   PubMedGoogle Scholar
• Carpenter MG, Feldmann M, Moury MT, et al. Objective Tests for Forward-Looking Pedestrian Crash Avoidance/Mitigation Systems. Washington, DC: NHTSA, US Department of Transportation; 2013. DOT HS 811 793.
   Google Scholar
• Clifton KJ, Burnier CV, Akar G. Severity of injury resulting from pedestrian–vehicle crashes: what can we learn from examining the built environment. Transp Res D Transp Environ. 2009;14:425–436.
   Web of Science ®Google Scholar
• Crandall JR, Bhalla KS, Madeley NJ. Designing road vehicles for pedestrian protection. BMJ. 2002;324:1145–1148.
   PubMed Web of Science ®Google Scholar
• Dai D. Identifying clusters and risk factors of injuries in pedestrian–vehicle crashes in a GIS environment. J Transp Geogr. 2012;24:206–214.
   Web of Science ®Google Scholar
• DI-Guy™  Software. Cambridge, MA: VT MAK Corporation; 2012.
   Google Scholar
• Du EY, Yang K, Jiang F, et al. , Seoul, Republic of Korea; 2013.
   Google Scholar
• Fitzpatrick K, Iragavarapu V, Chrysler ST, Park ES. Detection distances to crosswalk markings: transverse, continental and bar pairs. Transp Res Rec. 2011;2250:1–10.
   Web of Science ®Google Scholar
• Fleury D, Brenac T. Accident prototypical scenarios, a tool for road safety research and diagnostic studies. Accid Anal Prev. 2001;33:267–276.
   PubMed Web of Science ®Google Scholar
• Gholamjafari A, Li L, Chien S, Chen Y. Genetic algorithm-based testing scenarios selection for the performance evaluation of crash imminent braking systems for pedestrian safety. Paper presented at: IEEE Intelligent Transportation Systems Conference; October 2014.
   Google Scholar
• Jacobsen PL. Safety in numbers: more walkers and bicyclists, safer walking and bicycling. Inj Prev. 2003;9:205–209.
   PubMed Web of Science ®Google Scholar
• Jermakian JS. Crash avoidance potential of four passenger vehicle technologies. Accid Anal Prev. 2011;43:732–740.
   PubMed Web of Science ®Google Scholar
• Jermakian JS, Zuby DS. Primary Pedestrian Crash Scenarios: Factors Relevant to the Design of Pedestrian Detection Systems. Charlotesville, VA: Insurance Institute for Highway Safety; 2011.
   Google Scholar
• Mazzae EN, Baldwin GS, McGehee DV. Driver crash avoidance behavior with ABS in an intersection incursion scenario on the Iowa driving simulator. Paper presented at the Society of Automotive Engineers Annual Meeting; 1999.
   Google Scholar
• Naci H, Chisholm D, Baker TD. Distribution of road traffic deaths by road user group: a global comparison. Inj Prev. 2009;15:55–59.
   PubMed Web of Science ®Google Scholar
• Najm W, Smith J. Breakdown of light vehicle crashes by pre-crash scenarios as a basis for countermeasure development. In: Proceedings of the International Conference on Applications of Advanced Technologies in Transportation Engineering. 2002:712–719.
   Google Scholar
• Nasar J, Hecht P, Wener R. Mobile telephones, distracted attention, and pedestrian safety. Accid Anal Prev. 2008;40:69–75.
   PubMed Web of Science ®Google Scholar
• NHTSA. Traffic Safety Facts 2012 Pedestrians. Washington, DC: Author; 2014. DOT HS 811 888.
   Google Scholar
• Owens DA, Antonoff RJ, Francis EL. Biological motion and nighttime pedestrian conspicuity? Hum Factors. 1994;36:718–732.
   Web of Science ®Google Scholar
• Tian R, Li L, Yang K, Chien S, Chen Y, Sherony R. Estimation of the vehicle–pedestrian encounter/conflict risk on the road based on TASI 110-Car Naturalistic Driving data collection. Paper presented at: IEEE Intelligent Vehicles Symposium; June 2014.
   Google Scholar
• Yanagisawa M, Swanson E, Najm WG. Target Crashes and Safety Benefits Estimation Methodology for Pedestrian Crash Avoidance/Mitigation Systems. Washington, DC: NHTSA, US Department of Transportation; 2014. DOT-VNTSC-NHTSA-13-02.
   Google Scholar
• Zegeer CV, Bushell M. Pedestrian crash trends and potential countermeasures from around the world. Accid Anal Prev. 2012;44:3–11.
   PubMed Web of Science ®Google Scholar