5 papers
Latent Chain-of-Thought World Modeling for End-to-End Driving
Shuhan Tan, Kashyap Chitta, Yuxiao Chen +8
Recent Vision-Language-Action (VLA) models for autonomous driving explore inference-time reasoning as a way to improve driving performance and safety in challenging scenarios. Most…
Long-term Traffic Simulation with Interleaved Autoregressive Motion and Scenario Generation
Xiuyu Yang, Shuhan Tan, Philipp Krähenbühl
An ideal traffic simulator replicates the realistic long-term point-to-point trip that a self-driving system experiences during deployment. Prior models and benchmarks focus on clo…
SceneDiffuser++: City-Scale Traffic Simulation via a Generative World Model
Shuhan Tan, John Lambert, Hong Jeon +6
The goal of traffic simulation is to augment a potentially limited amount of manually-driven miles that is available for testing and validation, with a much larger amount of simula…
Interactive Post-Training for Vision-Language-Action Models
Shuhan Tan, Kairan Dou, Yue Zhao +1
We introduce RIPT-VLA, a simple and scalable reinforcement-learning-based interactive post-training paradigm that fine-tunes pretrained Vision-Language-Action (VLA) models using on…
Wolf: Dense Video Captioning with a World Summarization Framework
Boyi Li, Ligeng Zhu, Ran Tian +20
We propose Wolf, a WOrLd summarization Framework for accurate video captioning. Wolf is an automated captioning framework that adopts a mixture-of-experts approach, leveraging comp…