  Vehicles equipped with automated driving capabilities have shown the
potential to improve safety and operations. Advanced driver assistance systems
(ADAS) and automated driving systems (ADS) have been widely developed to
support vehicular automation. Although the studies on the injury severity
outcomes that involve automated driving systems are ongoing, there is limited
research investigating the difference between injury severity outcomes of the
ADAS and ADS vehicles using real-world crash data. To ensure comprehensive
analysis, a multi-source dataset that includes the NHTSA crash database (752
cases), CA DMV crash reports (498 cases), and news outlet data (30 cases) is
used. Two random parameters multinomial logit models with heterogeneity in the
means and variances are estimated to gain a better understanding of the
variables impacting the crash injury severity outcome for the ADAS (SAE Level
2) and ADS (SAE Levels 3-5) vehicles. We found that while 56 percent of crashes
involving ADAS vehicles took place on a highway, 84 percent of crashes
involving ADS took place in more urban settings. The model estimation results
indicate that the weather indicators, traffic incident or work zone indicator,
differences in the system sophistication that are captured by both manufacture
year and high or low mileage, type of collision, as well as rear and front
impact indicators all play a significant role in the crash injury severity. The
results offer an exploratory assessment of the safety performance of the ADAS
and ADS equipped vehicles in the real-world environment and can be used by the
manufacturers and other stakeholder to dictate the direction of their
deployment and usage.
