A multiscale framework for capturing oscillation dynamics of autonomous vehicles in data-driven car-following models

Recent advancements in machine learning-based car-following models have shown promise in leveraging vehicle trajectory data to accurately reproduce real-world driving behaviour in simulations. However, existing data-driven car-following models only explicitly consider individual vehicle trajectories for model training, overlooking broader traffic phenomena. This limitation hinders their ability to accurately capture the oscillation dynamics of vehicle platoons, which are critical for simulating and evaluating mesoscopic and macroscopic traffic phenomena such as congestion propagation, stop-and-go, string stability and hysteresis. To fill this gap, our study introduces a hybrid physical model-driven and data-driven framework, Multiscale Car-Following (MultiscaleCF), aimed at explicitly capturing mesoscopic oscillation dynamics within data-driven car-following models. MultiscaleCF offers two methodological advancements in the development of machine learning-based car-following models: the recursive simulation of a platoon of vehicles to reduce compound error and mesoscopic feature engineering using domain-specific attributes. Evaluated using the OpenACC database, the MultiscaleCF framework exhibited a simultaneous improvement in both microscopic and mesoscopic traffic simulation patterns. It outperforms the baseline model in microscopic trajectory prediction accuracy by up to 21%. For oscillation dynamics, it outperforms the baseline model by 42%, 32%, 29% and 42% in duration, amplitude, intensity, and hysteresis magnitude, respectively.