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

• URL: http://arxiv.org/abs/2503.23463v1
• Date: Sun, 30 Mar 2025 14:45:54 GMT
• Title: OpenDriveVLA: Towards End-to-end Autonomous Driving with Large Vision Language Action Model
• Authors: Xingcheng Zhou, Xuyuan Han, Feng Yang, Yunpu Ma, Alois C. Knoll,
• Abstract summary: We present OpenDriveVLA, a Vision-Language Action (VLA) model designed for end-to-end autonomous driving.<n>OpenDriveVLA builds upon open-source pre-trained large Vision-Language Models (VLMs) to generate reliable driving actions.
• Score: 24.90085777003393
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present OpenDriveVLA, a Vision-Language Action (VLA) model designed for end-to-end autonomous driving. OpenDriveVLA builds upon open-source pre-trained large Vision-Language Models (VLMs) to generate reliable driving actions, conditioned on 3D environmental perception, ego vehicle states, and driver commands. To bridge the modality gap between driving visual representations and language embeddings, we propose a hierarchical vision-language alignment process, projecting both 2D and 3D structured visual tokens into a unified semantic space. Besides, OpenDriveVLA models the dynamic relationships between the ego vehicle, surrounding agents, and static road elements through an autoregressive agent-env-ego interaction process, ensuring both spatially and behaviorally informed 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 OpenDriveVLA's superior capability to follow high-level driving commands and robustly generate trajectories under challenging scenarios, highlighting its potential for next-generation end-to-end autonomous driving. We will release our code to facilitate further research in this domain.

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