Less is More: Lean yet Powerful Vision-Language Model for Autonomous Driving

• URL: http://arxiv.org/abs/2510.00060v2
• Date: Fri, 03 Oct 2025 15:30:05 GMT
• Title: Less is More: Lean yet Powerful Vision-Language Model for Autonomous Driving
• Authors: Sheng Yang, Tong Zhan, Guancheng Chen, Yanfeng Lu, Jian Wang,
• Abstract summary: We introduce Max-V1, a novel framework for one-stage end-to-end autonomous driving.<n>Our framework presents a single-pass generation paradigm that aligns with the inherent sequentiality of driving.<n> Empirically, our method achieves the state-of-the-art performance on the nuScenes dataset.
• Score: 7.921556303360947
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work, we reconceptualize autonomous driving as a generalized language and formulate the trajectory planning task as next waypoint prediction. We introduce Max-V1, a novel framework for one-stage end-to-end autonomous driving. Our framework presents a single-pass generation paradigm that aligns with the inherent sequentiality of driving. This approach leverages the generative capacity of the VLM (Vision-Language Model) to enable end-to-end trajectory prediction directly from front-view camera input. The efficacy of this method is underpinned by a principled supervision strategy derived from statistical modeling. This provides a well-defined learning objective, which makes the framework highly amenable to master complex driving policies through imitation learning from large-scale expert demonstrations. Empirically, our method achieves the state-of-the-art performance on the nuScenes dataset, delivers an overall improvement of over 30% compared to prior baselines. Furthermore, it exhibits superior generalization performance on cross-domain datasets acquired from diverse vehicles, demonstrating notable potential for cross-vehicle robustness and adaptability. Due to these empirical strengths, this work introduces a model enabling fundamental driving behaviors, laying the foundation for the development of more capable self-driving agents. Code will be available upon publication.

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