27 citations · 39 across the 6 of their papers we have counts for
6 papers
WayveScenes101: A Dataset and Benchmark for Novel View Synthesis in Autonomous Driving
Jannik Zürn, Paul Gladkov, Sofía Dudas +5
We present WayveScenes101, a dataset designed to help the community advance the state of the art in novel view synthesis that focuses on challenging driving scenes containing many…
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…
LangProp: A code optimization framework using Large Language Models applied to driving
Shu Ishida, Gianluca Corrado, George Fedoseev +5
We propose LangProp, a framework for iteratively optimizing code generated by large language models (LLMs), in both supervised and reinforcement learning settings. While LLMs can g…
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…
GAIA-1: A Generative World Model for Autonomous Driving
Anthony Hu, Lloyd Russell, Hudson Yeo +5
Autonomous driving promises transformative improvements to transportation, but building systems capable of safely navigating the unstructured complexity of real-world scenarios rem…
Linking vision and motion for self-supervised object-centric perception
Kaylene C. Stocking, Zak Murez, Vijay Badrinarayanan +4
Object-centric representations enable autonomous driving algorithms to reason about interactions between many independent agents and scene features. Traditionally these representat…