The driving safety performance of automated driving system (ADS)-equipped
vehicles (AVs) must be quantified using metrics in order to be able to assess
the driving safety performance and compare it to that of human-driven vehicles.
In this research, driving safety performance metrics and methods for the
measurement and analysis of said metrics are defined and/or developed.
A comprehensive literature review of metrics that have been proposed for
measuring the driving safety performance of both human-driven vehicles and AVs
was conducted. A list of proposed metrics, including novel contributions to the
literature, that collectively, quantitatively describe the driving safety
performance of an AV was then compiled, including proximal surrogate indicators,
driving behaviors, and rules-of-the-road violations. These metrics, which
include metrics from on- and off-board data sources, allow the driving safety
performance of an AV to be measured in a variety of situations, including
crashes, potential conflicts, and near misses. These measurements enable the
evaluation of temporal flows and the quantification of key aspects of driving
safety performance. The identification and exploration of metrics focusing
explicitly on AVs as well as proposing a comprehensive set of metrics is a
unique contribution to the literature. The objective is to develop a concise set
of metrics that allow driving safety performance assessments to be effectively
made and that align with the needs of both the ADS development and
transportation engineering communities and accommodate differences in
cultural/regional norms.
Concurrent project work includes equipping an intersection with a sensor suite of
cameras, LIDAR, and RADAR to collect data requiring off-board sources and
employing test AVs to collect data requiring on-board sources. Additional
concurrent work includes development of artificial intelligence and computer
vision-based algorithms to automatically calculate the metrics using the
collected data. Future work includes using the collected data and algorithms to
finalize the list of metrics and then develop a methodology that uses the
metrics to provide an overall driving safety performance assessment score for an
AV.
