5 papers
StyleVAR: Controllable Image Style Transfer via Visual Autoregressive Modeling
Liqi Jing, Dingming Zhang, Peinian Li +3
We build on the Visual Autoregressive Modeling (VAR) framework and formulate style transfer as conditional discrete sequence modeling in a learned latent space. Images are decompos…
Behavior-Grounded Lane Representation Learning for Multi-Task Traffic Digital Twins
Rei Tamaru, Pei Li, Bin Ran
Traffic digital twins are powerful tools for advanced traffic management, and most systems are built on static geometric representations. However, these representations fail to cap…
Geo-ORBIT: A Federated Digital Twin Framework for Scene-Adaptive Lane Geometry Detection
Rei Tamaru, Pei Li, Bin Ran
Digital Twins (DT) have the potential to transform traffic management and operations by creating dynamic, virtual representations of transportation systems that sense conditions, a…
SEAL: Vision-Language Model-Based Safe End-to-End Cooperative Autonomous Driving with Adaptive Long-Tail Modeling
Junwei You, Pei Li, Zhuoyu Jiang +4
Autonomous driving technologies face significant safety challenges while operating under rare, diverse, and visually degraded weather scenarios. These challenges become more critic…
Planning Safety Trajectories with Dual-Phase, Physics-Informed, and Transportation Knowledge-Driven Large Language Models
Rui Gan, Pei Li, Keke Long +4
Foundation models have demonstrated strong reasoning and generalization capabilities in driving-related tasks, including scene understanding, planning, and control. However, they s…