Vehicle trajectory prediction serves as a critical enabler for autonomous navigation and intelligent transportation systems. While existing approaches predominantly focus on temporal pattern extraction and vehicle-environment interaction modeling, they exhibit a fundamental limitation in addressing trajectory heterogeneity originating from human driving styles. This oversight constrains prediction reliability in complex real-world scenarios. To bridge this gap, we propose the Driving-Style-Adaptive (\underline{\textbf{DSA}}) framework, which establishes the first systematic integration of heterogeneous driving behaviors into trajectory prediction models. Specifically, our framework employs a set of basis functions tailored to each driving style to approximate the trajectory patterns. By dynamically combining and adaptively adjusting the degree of these basis functions, DSA not only enhances prediction accuracy but also provides \textbf{explanations} insights into the prediction process. Extensive experiments on public real-world datasets demonstrate that the DSA framework outperforms state-of-the-art methods.

Multi-agent trajectory prediction is essential in autonomous driving, risk avoidance, and traffic flow control. However, the heterogeneous traffic density on interactions, which caused by physical laws, social norms and so on, is often overlooked in existing methods. When the density varies, the number of agents involved in interactions and the corresponding interaction probability change dynami-cally. To tackle this issue, we propose a new method, called Density-Adaptive Model based on Motif Matrix for Multi-Agent Trajectory Prediction (DAMM), to gain insights into multi-agent systems. Here we leverage the motif matrix to represent dynamic connectivity in a higher-order pattern, and distill the interaction information from the perspectives of the spatial and the temporal dimensions. Specifically, in spatial dimension, we utilize multi-scale feature fusion to adaptively select the optimal range of neighbors participating in interactions for each time slot. In temporal dimension, we extract the temporal interaction features and adapt a pyramidal pooling layer to generate the interaction probability for each agent. Experimental results demonstrate that our approach surpasses state-of-the-art methods on autonomous driving dataset.