Author ORCID Identifier
Date Available
7-19-2019
Year of Publication
2019
Degree Name
Master of Science in Civil Engineering (MSCE)
Document Type
Master's Thesis
College
Engineering
Department/School/Program
Civil Engineering
First Advisor
Dr. Reginald R. Souleyrette
Abstract
The Crash Modification Factor (CMF) clearinghouse can be used to estimate benefits for specific highway safety countermeasures. It assists safety professionals in the allocation of investments. The clearinghouse contains over 7000 entries of which only 446 are categorized as intelligent transportation systems or advanced technology, but none directly address connected or autonomous vehicles (CAVs). Further, the effectiveness of highway safety countermeasures is assumed to remain constant over time, an assumption that is particularly problematic as new technologies are introduced. For example, for the existing fleet of human-driven vehicles, installation of rumble strip can potentially reduce “run-off-road” crashes by 40%. If specific CAV technologies, e.g., lane-tracking, can work without rumble strips, and say, half of all cars are so equipped, only half of the fleet will benefit, reducing the benefits of rumble strips by a commensurate amount. Benefits of the two improvements, e.g., rumble strips and automated vehicles, should not be double-counted. As there will still be human-driven and/or non-connected vehicles in the fleet, conventional countermeasures are still necessary, although returns on conventional safety investments may be significantly overestimated. This is important as safety investments should be optimized and geared to future, not past fleets. Moreover, as CMFs are based on historical events, the types of crashes experienced by human-driven, un-connected cars are likely to be much different in the future. This research presents methods to estimate the safety benefits that autonomous vehicles have to offer and the changes needed in CMFs as a result of their adoption. This will primarily be achieved by modifying and enhancing a tool co-developed by the Fellow that estimates the safety benefits of different levels of autonomy. This tool, ddSAFCAT, estimates CAV safety benefits using real-world data for crashes, market penetration, and effectiveness.
Digital Object Identifier (DOI)
https://doi.org/10.13023/etd.2019.356
Funding Information
This research was supported by the Dwight D. Eisenhower Transportation Fellowship Program 3200002311.
Recommended Citation
Lause, Federico Valentin III, "Adapting Crash Modification Factors for the Connected and Autonomous Vehicle Environment" (2019). Theses and Dissertations--Civil Engineering. 90.
https://uknowledge.uky.edu/ce_etds/90
