As the deployment of Automated Driving Systems (ADS) and Advanced Driver Assistance Systems (ADAS-L2) expands, understanding their real-world safety performance becomes essential. This study examines the severity and contributing factors of crashes involving vehicles equipped with ADS and ADAS-L2 technologies using NHTSA data. Using machine learning models on crash datasets from 2021 to 2024, this research identifies patterns and risk factors influencing injury outcomes. After data preprocessing and handling missing values for severity classification, four models were trained: logistic regression, random forest, SVM, and XGBoost. XGBoost outperformed the others for both ADS and ADAS-L2, achieving the highest accuracy and recall. Variable importance analysis showed that for ADS crashes, interactions with other road users and poor lighting were the strongest predictors of injury severity, while for ADAS-L2 crashes, fixed object collisions and low light conditions were most influential. From a policy and engineering perspective, this study highlights the need for standardized crash reporting and improved ADS object detection and pedestrian response. It also emphasizes effective human–machine interface design and driver training for partial automation. Unlike previous research, this study conducts comparative model-based evaluations of both ADS and ADAS-L2 using recent crash reports to inform safety standards and policy frameworks.

As Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS) technologies become more prevalent, evaluating their safety performance in complex, real-world environments has emerged as a critical research priority. This study presents a systematic literature review of crashes involving vehicles equipped with ADAS and ADS. Using the PRISMA methodology, this review analyzed 51 peer-reviewed articles and books published between 2018 and 2025. Articles were identified through comprehensive searches of ScienceDirect, TRID, and the Transportation Research Record. The selected studies were organized into five thematic categories: safety and crash characteristics, human interaction and user behavior, testing tools and simulations, cybersecurity and perception risks, and policy and governance. Findings reveal that while AV technologies show promise in reducing crash severity and frequency, performance is highly context-dependent, affected by roadway geometry, weather, system limitations, and user behavior. ADAS-related crashes are more common on highways and often involve human error, whereas ADS crashes are more likely in urban settings and linked to system disengagements. The review also highlights significant gaps in data availability, standardized evaluation protocols, and human-machine interaction research. Despite growing literature, many studies rely heavily on simulations and legacy datasets, limiting generalizability. To support safer AV deployment, future research must prioritize real-world testing and user trust frameworks deployment strategies. This review offers a knowledge base to guide policymakers, researchers, and industry stakeholders in advancing the development and regulation of vehicle automation technologies.

Bicycling is an important mode of sustainable and active transportation, but bicycle safety remains a major concern in urban areas, especially at intersections where cyclists interact with turning vehicles, crossing traffic, and complex roadway conditions. This study assesses the effect of roadway, environmental, and infrastructure-related factors on bicycle safety at intersections in Baltimore City. Crash data from 2022 to 2024 were obtained from the Maryland crash data records and analyzed for bicycle-involved crashes at intersections. The study used descriptive statistics, GIS-based spatial analysis, visualizations, and exploratory regression models, including linear regression, binary logistic regression, and multinomial logistic regression. The results showed that Baltimore City had 180 bicycle-involved crashes at intersections during the study period, most of which resulted in injury. Spatial analysis indicated that crashes were concentrated mainly in downtown Baltimore. Descriptive results showed that many crashes occurred during daylight, clear weather, and dry surface conditions, which may reflect higher bicycle activity during these periods. The Sankey diagram suggested that severe crash outcomes were more common in locations without bike lanes. However, the regression models did not identify statistically significant relationships between the selected variables and crash severity. The findings highlight the need for better bicycle exposure data, more complete infrastructure variables, and improved intersection-level safety planning in Baltimore City.