Abstract "This project will focus on understanding how safety-related metrics will likely change
after the deployment of connected and automated vehicles (CAVs). As part of this
project, first we will investigate the capabilities of the CAVs that are currently developed
to date. Next, we will identify which safety metrics and associated performance
measures, will be impacted by the deployment of new technologies, such as number of
voluntary or mandatory take overs, crash frequency, etc. Next, we will investigate the
operational domains and scenarios that these measures will be impacted and determine
the magnitude of expected impact. We will also estimate the expected thresholds of the
safety performance metrics. For example, the time to collision (TTC) threshold may be
significantly lower for CAV/human-driven vehicles than for pairs of human-driven
vehicles. The findings of this study will be important for state DOT or local agencies to
evaluate the safety performance of their highway systems."
Description "SSM used for both CAV and HDV. The literature will also summarize the CAV
technologies that were assumed in past research, as well as operational domains.
Finally, the literature review will also include a detailed description of the types and the
characteristics of the crash prediction models used in SSM and SSM-based research.
Task 2: Data Collection and SSM Analysis: During this task, the team will obtain
trajectory data from online available datasets that include limited CAV data. An example
of such dataset is the Waymo data (https://waymo.com/open/), which contains car
following pair trajectories between a fully automated vehicle (AV) and HDV or between
HDV. This is a good candidate dataset as it involves multiple geometric and
environmental conditions for 1,000 trajectory pairs at 0.1s intervals. The research team
will also try to obtain data that include interconnected vehicles. One example is testbeds
that the Federal Highway Administration (FHWA) is currently deploying. Several
different SSM will be calculated based on the available data.
Task 3: Simulation of Specific Conditions: During this task, the research team will
use simulation to generate additional vehicle trajectories for all three pairs, for specific
geometric and environmental domains (e.g., freeways under good weather and sunny
conditions). The simulation models will be calibrated using the field data. The Surrogate
Safety Assessment Model (SSAM) will be used to analyze the trajectories and compute
SSM. A comparison between the SSM that include CAV or only HDV will be conducted
to evaluate the differences and capture the impact on automation on conflict research.
Task 4: Final Report: The research team will compose the Final Report that describes
the efforts undertaken during the final stage of the project."
Objective "The expected results of this research project are a set of new or modified surrogate
safety measures and their respective threshold values, specifically for connected and
automated vehicles."
Impacts/Benefits "Undergraduate and graduate students at the University of Kansas will be trained on
highway safety topics. Students will be able to better understand safety analysis
procedures as well as the implications of CAVs on safety. The findings of this research
can also be used to inform policies related to the adoption of CAV technology and
provide realistic expectations on the safety benefits stemming from CAVs."