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arXiv · 2503.23463

OpenDriveVLA: Towards End-to-end Autonomous Driving with Large Vision Language Action Model

Xingcheng Zhou·Xuyuan Han·Feng Yang·Yunpu Ma·Volker Tresp·Alois Knoll
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Abstract

We present OpenDriveVLA, a Vision Language Action model designed for end-to-end autonomous driving, built upon open-source large language models. OpenDriveVLA generates spatially grounded driving actions by leveraging multimodal inputs, including 2D and 3D instance-aware visual representations, ego vehicle states, and language commands. To bridge the modality gap between driving visual representations and language embeddings, we introduce a hierarchical vision language alignment process, projecting both 2D and 3D structured visual tokens into a unified semantic space. Furthermore, we incorporate structured agent environment ego interaction modeling into the autoregressive decoding process, enabling the model to capture fine-grained spatial dependencies and behavior-aware dynamics critical for reliable trajectory planning. Extensive experiments on the nuScenes dataset demonstrate that OpenDriveVLA achieves state-of-the-art results across open-loop trajectory planning and driving-related question answering tasks. Qualitative analyses further illustrate its capability to follow high-level driving commands and generate trajectories under challenging scenarios, highlighting its potential for next-generation end-to-end autonomous driving.

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Xingcheng Zhou, Xuyuan Han, Feng Yang, Yunpu Ma, Volker Tresp, Alois Knoll. 2025-11-21. OpenDriveVLA: Towards End-to-end Autonomous Driving with Large Vision Language Action Model. https://arxiv.org/abs/2503.23463

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