4 papers
Outcome-Guided Distillation: A Teacher-Student Framework to Advance VLM Reasoning in Autonomous Driving
Zeyu Dong, Yimin Zhu, Yu Wu +1
End-to-end (E2E) autonomous driving aims to learn a direct mapping from visual observations to control actions. However, these E2E models often act as black boxes and struggle with…
Adaptive Model-Based Reinforcement Learning for Orbit Feedback Control in NSLS-II Storage Ring
Zeyu Dong, Yuke Tian, Yu Sun
The National Synchrotron Light Source II (NSLS-II) uses highly stable electron beam to produce high-quality X-ray beams with high brightness and low-emittance synchrotron radiation…
FROST-Drive: Scalable and Efficient End-to-End Driving with a Frozen Vision Encoder
Zeyu Dong, Yimin Zhu, Yu Wu +1
End-to-end (E2E) models in autonomous driving aim to directly map sensor inputs to control commands, but their ability to generalize to novel and complex scenarios remains a key ch…
Generalizing End-To-End Autonomous Driving In Real-World Environments Using Zero-Shot LLMs
Zeyu Dong, Yimin Zhu, Yansong Li +2
Traditional autonomous driving methods adopt a modular design, decomposing tasks into sub-tasks. In contrast, end-to-end autonomous driving directly outputs actions from raw sensor…