Abstract Autonomous driving based on single-vehicle perception still cannot avoid the negative impacts caused by traffic oscillations when traveling in mixed-vehicle platoons. In this scenario, real-time and accurate prediction of the future trajectories of the vehicle platoon ahead of the autonomous vehicle is key to addressing this issue. However, in mixed platoons dominated by heterogeneous and uncertain human-driven vehicles (HDVs), this remains a significant challenge. Existing model-driven, data-driven, and hybrid approaches often suffer from poor interpretability, weak physical constraints, and insufficient accuracy in dynamic environments. To overcome these limitations, this study proposes a Causal Physical Information Deep Learning Model (CPIDLM) for high-fidelity platoon trajectory prediction. First, CPIDLM constructs a novel causal graph attention mechanism that explicitly captures behavioral heterogeneity and causal interactions among vehicles. Second, a physics-informed enhancement architecture is developed to embed prior knowledge from physical models into the deep learning network. Additionally, a dynamic adaptive weighting module is designed to achieve a dynamic balance between contributions from physical laws and data-driven patterns. Extensive validation based on real-world trajectory datasets shows that, compared to existing models, CPIDLM reduces gap prediction error by 16.7%, significantly outperforming current state-of-the-art methods in accuracy. This study establishes a powerful new paradigm for vehicle platoon trajectory prediction under traffic oscillation scenarios.
Li et al. (2026) studied this question.