How to improve the AV safety: From the understanding of AV crashes to safety enhancement strategy

Autonomous vehicles (AVs) are increasingly prioritized in national transport strategies for their potential to improve safety and sustainability. Yet real-world crashes show that AVs remain vulnerable to safety risks, highlighting the need to understand their crash patterns and develop evidence-based countermeasures. The objective of this study is to investigate the factors associated with AV crashes and develop safety countermeasure to improve AV safety. 335 AV crash data from 2021 to 2022 was collected from collision reports from San Francisco at Traffic Analysis Zones (TAZ) level. Sociodemographic, built environment, land-use, and exposure variables were incorporated into Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) models. Compared with OLS, the GWR model provides a superior fit, as reflected by its lower AIC (535.17) and residual sum of squares (580.66). Model results revealed diverse effects of build environment on AV crashes across TAZs. Specifically, bus stop and transit lane densities exhibit strong negative associations with crash frequency, particularly in the southwestern regions. Bicycle parking density is negatively correlated with crashes. In contrast, wider sidewalks and higher proportions of speed limit zones are positively associated with AV crashes in certain urban areas. The impact of traffic signal density is spatially inconsistent—showing a crash-reducing effect in northeastern urban areas but a positive association in southwestern regions. Safety countermeasures were proposed from the perspective of understanding the AV crash influencing factors. The study underscores the significance of well-planned transportation facilities in enhancing AV safety.