9 papers
Metis: A Generalizable and Efficient World-Action Model for Autonomous Driving and Urban Navigation
Jingyu Li, Zhe Liu, Dongnan Hu +10
World action models~(WAMs) have shown great promise for autonomous driving and urban navigation. Built upon Vision-Language-Action models or video generation models, existing appro…
MCNav: Memory-Aware Dynamic Cognitive Map for Zero-shot Goal-oriented Navigation
Jingyu Li, Zhe Liu, Wenxiao Wu +1
Navigating to instance-level targets in complex environments is a challenging problem. Many existing zero-shot methods achieve strong performance by modeling the entire environment…
Uni-World VLA: Interleaved World Modeling and Planning for Autonomous Driving
Qiqi Liu, Huan Xu, Jingyu Li +5
Autonomous driving requires reasoning about how the environment evolves and planning actions accordingly. Existing world-model-based approaches typically predict future scenes firs…
ImagiDrive: A Unified Imagination-and-Planning Framework for Autonomous Driving
Jingyu Li, Bozhou Zhang, Xin Jin +3
Autonomous driving requires rich contextual comprehension and precise predictive reasoning to navigate dynamic and complex environments safely. Vision-Language Models (VLMs) and Dr…
UniMotion: A Unified Motion Framework for Simulation, Prediction and Planning
Nan Song, Junzhe Jiang, Jingyu Li +2
Motion simulation, prediction and planning are foundational tasks in autonomous driving, each essential for modeling and reasoning about dynamic traffic scenarios. While often addr…
SGDrive: Scene-to-Goal Hierarchical World Cognition for Autonomous Driving
Jingyu Li, Junjie Wu, Dongnan Hu +6
Recent end-to-end autonomous driving approaches have leveraged Vision-Language Models (VLMs) to enhance planning capabilities in complex driving scenarios. However, VLMs are inhere…