9 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…
TGIF: Text-Guided Layer Fusion Mitigates Hallucination in Multimodal LLMs
Chenchen Lin, Sanbao Su, Rachel Luo +4
Multimodal large language models (MLLMs) typically rely on a single late-layer feature from a frozen vision encoder, leaving the encoder's rich hierarchy of visual cues under-utili…
Accelerating Structured Chain-of-Thought in Autonomous Vehicles
Yi Gu, Yan Wang, Yuxiao Chen +8
Chain-of-Thought (CoT) reasoning enhances the decision-making capabilities of vision-language-action models in autonomous driving, but its autoregressive nature introduces signific…
Towards Efficient and Effective Multi-Camera Encoding for End-to-End Driving
Jiawei Yang, Ziyu Chen, Yurong You +7
We present Flex, an efficient and effective scene encoder that addresses the computational bottleneck of processing high-volume multi-camera data in end-to-end autonomous driving.…
dVLM-AD: Enhance Diffusion Vision-Language-Model for Driving via Controllable Reasoning
Yingzi Ma, Yulong Cao, Wenhao Ding +6
The autonomous driving community is increasingly focused on addressing the challenges posed by out-of-distribution (OOD) driving scenarios. A dominant research trend seeks to enhan…
Efficient Multi-Camera Tokenization with Triplanes for End-to-End Driving
Boris Ivanovic, Cristiano Saltori, Yurong You +3
Autoregressive Transformers are increasingly being deployed as end-to-end robot and autonomous vehicle (AV) policy architectures, owing to their scalability and potential to levera…