Fleetwide Models of Lane Departure Warning and Prevention Systems in the United States
| dc.contributor.author | Johnson, Taylor | en |
| dc.contributor.committeechair | Gabler, Hampton Clay | en |
| dc.contributor.committeemember | McLaughlin, Shane B. | en |
| dc.contributor.committeemember | Rowson, Steven | en |
| dc.contributor.department | Biomedical Engineering | en |
| dc.date.accessioned | 2017-02-09T18:28:07Z | en |
| dc.date.available | 2017-02-09T18:28:07Z | en |
| dc.date.issued | 2017-02-09 | en |
| dc.description.abstract | Road departure crashes are among the deadliest crash modes in the U.S. each year. In response, automakers have been developing lane departure active safety systems to alert drivers to impending departures. These lane departure warning (LDW) and lane departure prevention (LDP) systems have great potential to reduce the frequency and mitigate the severity of serious lane and road departure crashes. The objective of this thesis was to characterize lane and road departures to better understand the effect of systems such as LDW and LDP on single vehicle road departure crashes. The research includes the following: 1) a characterization of lane departures through analysis of normal lane keeping behavior, 2) a characterization of road departure crashes through the development and validation of a real-world crash database of road departures (NCHRP 17-43 Lite), and 3) develop enhancements to the Virginia Tech LDW U.S. fleetwide benefits model. Normal lane keeping behavior was found to vary with road characteristics such as lane width and road curvature. Consideration of the dynamic driving behaviors observed in the naturalistic driving study (NDS) data is important to avoid LDW false alarms and driver annoyance. Departure characteristics computed in normal driving were much less severe than the departure parameters measured in real-world road departure crashes. The real-world crash data collected in NCHRP 17-43 Lite database was essential in developing enhancements to the existing Virginia Tech LDW fleetwide benefits model. Replacement of regression model predictions with measured crash data and improvement of the injury criteria resulted in an 11-16% effectiveness for road departure crashes, and an 11-15% reduction in seriously injured drivers. | en |
| dc.description.degree | Master of Science | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:9400 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/74981 | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | vehicle safety | en |
| dc.subject | active safety | en |
| dc.subject | road departure | en |
| dc.subject | lane keeping | en |
| dc.title | Fleetwide Models of Lane Departure Warning and Prevention Systems in the United States | en |
| dc.type | Thesis | en |
| thesis.degree.discipline | Biomedical Engineering | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | masters | en |
| thesis.degree.name | Master of Science | en |
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