Youfang Lin

TrajAR: Long-Term Trajectory Prediction at Urban Intersections via Multi-scale Interaction Perception

Low-Rank Prior-Induced Consistency Flow Matching for Efficient Traffic Imputation

Generative models like diffusion and flow matching excel in traffic imputation but suffer from high inference latency, as the learned vector fields typically induce curved generative trajectories requiring multi-step numerical integration. Furthermore, learning the transformation from non-informative priors introduces redundant computational overhead. To address these issues, we propose Low-Rank Prior-Induced Consistency Flow Matching (LOFT) for efficient and effective distribution modeling under highly sparse data. First, we construct a low-rank prior from sparse observations to recover inherent spatiotemporal correlations. Initializing the flow with this informative prior reduces the mapping complexity, allowing the model to focus on fine-grained variations. Second, to enable efficient inference by linearizing generative trajectories, we introduce an uncertainty-aware rectification mechanism. This mechanism resolves the gradient conflict between improving accuracy and trajectory linearization by dynamically arbitrating the optimization trade-off based on the training progress and data uncertainty. Experimental results demonstrate that LOFT surpasses state-of-the-art baselines using an NFE (Number of Function Evaluations) of 2, whereas competing methods typically require 20 to 50 NFE, achieving over a 10× improvement in inference efficiency. The code is available at https://github.com/maoxiaowei97/LOFT.

G-VTM: A Multimodal Vision-Trajectory Model for Generalized Vehicle Trajectory Prediction

G-VTM, a generalized vision-trajectory model, is proposed, which captures global map semantics while modeling scenario-and direction-aware interaction based on intuitive visual perception and achieves strong generalized performance under heterogeneous traffic conditions.

Tracking Topological Shifts: How Can Dynamic Graph Invariant Learning Enable Reliable Out-of-Time Spatio-Temporal Prediction?

DynaSTar is proposed, a Dynamic Spatio-Temporal Graph Invariant Learning model designed for reliable out-of-time (OOT) traffic prediction under evolving topologies, which employs a dynamic probabilistic graph structure, which is continuously refined through momentum-based updates and differentiable sparse sampling to model evolving inter-node dependencies.

Low-Rank Prior-Induced Consistency Flow Matching for Efficient Traffic Imputation

Low-Rank Prior-Induced Consistency Flow Matching (LOFT) is proposed for efficient and effective distribution modeling under highly sparse data, and introduces an uncertainty-aware rectification mechanism to enable efficient inference by linearizing generative trajectories.