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Vehicles equipped with automated driving capabilities have shown thepotential to improve safety and operations. Advanced driver assistance systems(ADAS) and automated driving systems (ADS) have been widely developed tosupport vehicular automation. Although the studies on the injury severityoutcomes that involve automated driving systems are ongoing, there is limitedresearch investigating the difference between injury severity outcomes of theADAS and ADS vehicles using real-world crash data. To ensure comprehensiveanalysis, a multi-source dataset that includes the NHTSA crash database (752cases), CA DMV crash reports (498 cases), and news outlet data (30 cases) isused. Two random parameters multinomial logit models with heterogeneity in themeans and variances are estimated to gain a better understanding of thevariables impacting the crash injury severity outcome for the ADAS (SAE Level2) and ADS (SAE Levels 3-5) vehicles. We found that while 56 percent of crashesinvolving ADAS vehicles took place on a highway, 84 percent of crashesinvolving ADS took place in more urban settings. The model estimation resultsindicate that the weather indicators, traffic incident or work zone indicator,differences in the system sophistication that are captured by both manufactureyear and high or low mileage, type of collision, as well as rear and frontimpact indicators all play a significant role in the crash injury severity. Theresults offer an exploratory assessment of the safety performance of the ADASand ADS equipped vehicles in the real-world environment and can be used by themanufacturers and other stakeholder to dictate the direction of theirdeployment and usage.

Exploratory analysis of injury severity under different levels of driving automation (SAE Level 2-5) using multi-source data