6 papers
DriveMA: Driving Vision-Language-Action Models with verifiable Meta-Actions
Weicheng Zheng, Yixin Huang, Qiao Sun +2
Driving Vision-Language-Action Models (Driving VLAs) aim to use language to improve end-to-end planning, but the language-action gap limits this promise. We propose DriveMA, a Driv…
Is Your Trajectory Displacement Safe in Long-tail?
Qiao Sun, Weicheng Zheng, Yixin Huang +1
Long-tail scenarios remain a major bottleneck for autonomous driving evaluation, even as datasets grow by orders of magnitude. Existing evaluation pipelines are rarely human-aligne…
DriveMA: Rethinking Language Interfaces in Driving VLAs with One-Step Meta-Actions
Weicheng Zheng, Yixin Huang, Qiao Sun +2
Driving Vision-Language-Action Models (Driving VLAs) commonly introduce natural-language reasoning as an intermediate interface for end-to-end planning, but reasoning-centric inter…
Action Images: End-to-End Policy Learning via Multiview Video Generation
Haoyu Zhen, Zixian Gao, Qiao Sun +7
World action models (WAMs) have emerged as a promising direction for robot policy learning, as they can leverage powerful video backbones to model the future states. However, exist…
Generalizing Motion Planners with Mixture of Experts for Autonomous Driving
Qiao Sun, Huimin Wang, Jiahao Zhan +7
Large real-world driving datasets have sparked significant research into various aspects of data-driven motion planners for autonomous driving. These include data augmentation, mod…
Uncertainty-Aware Decision Transformer for Stochastic Driving Environments
Zenan Li, Fan Nie, Qiao Sun +2
Offline Reinforcement Learning (RL) enables policy learning without active interactions, making it especially appealing for self-driving tasks. Recent successes of Transformers ins…