Real-World Data-Driven Analysis of Environment- and Context-Dependent Driving Behavior

Understanding and modeling human driving behavior is essential to improve advanced driver assistance systems (ADAS) and traffic simulations. Traditional driver models are often limited in capturing context- and environment-dependent variations due to simplifying assumptions. The increasing availability of real-world driving data from connected vehicles enables the identification of context-adaptive model parameters, provided that strict privacy and anonymization measures are observed. We analyze data from 156 electric vehicles, each equipped with high-frequency data loggers, recording over 2000 signals per vehicle during a six-month period in uncontrolled environments. As a case study, we examine the approach behavior of a preceding vehicle, which is important for adaptive cruise control (ACC), and its dependence on temperature, light levels, and rain. Our findings suggest that cold temperatures and nighttime conditions increase time-headway and following distances, while the effects of rain appear less pronounced. In the long term, such datasets could refine context-aware driver models for simulation and ADAS calibration and enable personalized adaptive cruise control based on learned driving preferences. By integrating context- and environment-aware driver models, this approach bridges the gap between theoretical modeling and real-world driving behavior.