13 citations · 13 across the 7 of their papers we have counts for
8 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…
Goal-based Neural Physics Vehicle Trajectory Prediction Model
Rui Gan, Haotian Shi, Pei Li +6
Vehicle trajectory prediction plays a vital role in intelligent transportation systems and autonomous driving, as it significantly affects vehicle behavior planning and control, th…
Enhancing Pedestrian Trajectory Prediction with Crowd Trip Information
Rei Tamaru, Pei Li, Bin Ran
Pedestrian trajectory prediction is essential for various applications in active traffic management, urban planning, traffic control, crowd management, and autonomous driving, aimi…
Demystifying the Physics of Deep Reinforcement Learning-Based Autonomous Vehicle Decision-Making
Hanxi Wan, Pei Li, Arpan Kusari
With the advent of universal function approximators in the domain of reinforcement learning, the number of practical applications leveraging deep reinforcement learning (DRL) has e…