Cross-Cultural Analysis of Car-Following Dynamics: A Comparative Study of Open-Source Trajectory Datasets

This study addresses the critical need for refined, reliable, and complete real-world trajectory data in the de-velopment of Advanced Driver Assistance Systems (ADAS), particularly for Adaptive Cruise Control (ACC) functions. We conducted a comprehensive comparison of car-following and deceleration scenarios across ten open-source datasets from multiple countries, encompassing both highway and urban environments. Focusing on key kinematic variables crucial for longitudinal behavior, we employed statistical measures and safety metrics to compare data sets across different driving regulations and road designs. Our findings reveal substantial overlaps in the distributions of logical parameters, despite the varied data sources and cultural contexts. However, we noted significant differences in safety-critical metrics, such as Time Headway and Time To Collision (TTC), highlighting culture-specific driving behaviors. Interestingly, Chinese datasets consistently exhibited the smallest distance head ways across all scenarios, yet maintained high TTC values (around 16s) compared to other datasets, suggesting a unique approach to risk management. To quantify these differences, we calibrated the Intelligent Driver Model using U.S. data and evaluated its transferability, demonstrating remarkable performance degradation when applied to non-U.S. datasets. These results provide crucial insights for developing globally applicable, yet culturally sensitive safety assessment methodologies for next-generation automated vehicles, highlighting the need for adaptive ADAS technologies that can accommodate regional driving norms while maintaining consistent safety standards. The code and extracted Longitudinal Trajectory data used in this study are available: https://github.com/imanetaourarti/Car-Following-analysis.