Kaiyuan Tan — accepted AI-conference papers

2026

UFO: Unifying Feed-Forward and Optimization-based Methods for Large Driving Scene Modeling

Kaiyuan Tan, Yingying Shen, Ziyue Zhu, Mingfei Tu, Haohui Zhu, Haiyang Sun +3

CVPR 2026

Dynamic driving scene modeling is critical for autonomous driving simulation and closed-loop learning. While recent feed-forward methods offer fast inference through data-driven priors, they struggle with long-range driving sequences due to quadratic complexity in sequence length and restrictive ass

Cited by 0 Source ↗Scholar ↗

2025

Plug-and-Play Physics-Informed Learning Using Uncertainty Quantified Port-Hamiltonian Models

Kaiyuan Tan, Peilun Li, Jun Wang, Thomas Beckers

ICRA 2025

The ability to predict trajectories of surrounding agents and obstacles is a crucial component in many robotic applications. Data-driven approaches are commonly adopted for state prediction in scenarios where the underlying dynamics are unknown. However, the performance, reliability, and uncertainty

Cited by 2 Source ↗Scholar ↗