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. / Master of Science / Road departure crashes account for nearly one-third of the roughly 30,000 automobile traffic fatalities in the U.S. each year. Lane departure warning (LDW) and lane departure prevention (LDP) systems are two safety systems developed to reduce the large number of fatalities resulting from road departures. The safety systems warn drivers if the vehicle begins to drift out of the intended lane of travel, and automatically steer the vehicle back into the lane of travel if it continues to drift. While LDW and LDP systems have potential to lower the number of fatal lane and road departure crashes, the technology is not yet a standard feature in production vehicles. There has been a lower than expected acceptance rate, and real-world benefits of the systems have not been published.
The research objective for this thesis was to characterize lane and road departures to investigate the effect of these safety systems on road departure crashes. The first section of this thesis analyzed large amounts of time series data recorded from people in normal driving scenarios to model lane keeping behavior in non-crash, drift out of lane departures. We found driving behavior varied with road characteristics such as lane width and road curvature. These dynamic driving behaviors may lead to LDW false alarms and contribute to driver annoyance with the systems.
The second portion of this research involved the development and validation of a real-world road departure crash database. The database included key departure parameters such as angle, speed, and road curvature. These parameters were used in the third section of the thesis to enhance the Virginia Tech LDW U.S. fleetwide benefits model, which is a mathematical trajectory simulation model that determines whether or not these road departure crashes could have been prevented if every vehicle in the U.S. was equipped with LDW. We found an effectiveness of 11-16% prevention for road departure crashes, and an 11-15% reduction in serious driver injury.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/74981 |
| Date | 09 February 2017 |
| Creators | Johnson, Taylor |
| Contributors | Biomedical Engineering, Gabler, Hampton Clay, McLaughlin, Shane B., Rowson, Steven |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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