6 citations · 16 across the 6 of their papers we have counts for
6 papers
DriveFine: Refining-Augmented Masked Diffusion VLA for Precise and Robust Driving
Chenxu Dang, Sining Ang, Yongkang Li +7
Vision-Language-Action (VLA) models for autonomous driving increasingly adopt generative planners trained with imitation learning followed by reinforcement learning. Diffusion-base…
Fast-Slow Efficient Training for Multimodal Large Language Models via Visual Token Pruning
Dingkun Zhang, Shuhan Qi, Yulin Wu +3
Multimodal Large Language Models (MLLMs) suffer from severe training inefficiency issue, which is associated with their massive model sizes and visual token numbers. Existing effor…
SimLingo: Vision-Only Closed-Loop Autonomous Driving with Language-Action Alignment
Katrin Renz, Long Chen, Elahe Arani +1
Integrating large language models (LLMs) into autonomous driving has attracted significant attention with the hope of improving generalization and explainability. However, existing…
CarLLaVA: Vision language models for camera-only closed-loop driving
Katrin Renz, Long Chen, Ana-Maria Marcu +6
In this technical report, we present CarLLaVA, a Vision Language Model (VLM) for autonomous driving, developed for the CARLA Autonomous Driving Challenge 2.0. CarLLaVA uses the vis…
LingoQA: Visual Question Answering for Autonomous Driving
Ana-Maria Marcu, Long Chen, Jan Hünermann +9
We introduce LingoQA, a novel dataset and benchmark for visual question answering in autonomous driving. The dataset contains 28K unique short video scenarios, and 419K annotations…
Driving with LLMs: Fusing Object-Level Vector Modality for Explainable Autonomous Driving
Long Chen, Oleg Sinavski, Jan Hünermann +5
Large Language Models (LLMs) have shown promise in the autonomous driving sector, particularly in generalization and interpretability. We introduce a unique object-level multimodal…