3 papers
cs.AI2026
K-Gen: A Multimodal Language-Conditioned Approach for Interpretable Keypoint-Guided Trajectory Generation
Mingxuan Mu, Guo Yang, Lei Chen +2
Generating realistic and diverse trajectories is a critical challenge in autonomous driving simulation. While Large Language Models (LLMs) show promise, existing methods often rely…
cs.RO2026
SaFeR: Safety-Critical Scenario Generation for Autonomous Driving Test via Feasibility-Constrained Token Resampling
Jinlong Cui, Fenghua Liang, Guo Yang +2
Safety-critical scenario generation is crucial for evaluating autonomous driving systems. However, existing approaches often struggle to balance three conflicting objectives: adver…
cs.LG2025
STGAtt: A Spatial-Temporal Unified Graph Attention Network for Traffic Flow Forecasting
Zhuding Liang, Jianxun Cui, Qingshuang Zeng +3
Accurate and timely traffic flow forecasting is crucial for intelligent transportation systems. This paper presents a novel deep learning model, the Spatial-Temporal Unified Graph…