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Related Experiment Video

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VLA-MP: A Vision-Language-Action Framework for Multimodal Perception and Physics-Constrained Action Generation in

Maoning Ge1, Kento Ohtani1, Yingjie Niu1

  • 1Graduate School of Informatics, Nagoya University, Furo-cho, Chikusa-Ward, Nagoya 464-8601, Japan.

Sensors (Basel, Switzerland)
|October 16, 2025
PubMed
Summary
This summary is machine-generated.

This study presents VLA-MP, a vision-language-action framework for autonomous driving. It enhances perception, language understanding, and physics-informed planning for safer, more robust navigation in complex environments.

Keywords:
Vision-Language-Action modelsautonomous drivinglarge language modelsmultimodal perceptiontrajectory planning

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Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Autonomous driving systems struggle with complex environments, requiring advanced perception, reasoning, and planning.
  • Current end-to-end approaches face limitations in robustness and physical feasibility.

Purpose of the Study:

  • To introduce VLA-MP, a unified vision-language-action framework for autonomous driving.
  • To enhance the integration of multimodal perception, language understanding, and physics-informed action generation.

Main Methods:

  • VLA-MP integrates multimodal Bird's-Eye View (BEV) perception, vision-language alignment, and a GRU-bicycle dynamics cascade adapter.
  • It processes RGB images and LiDAR for environmental representation, aligns scene features with natural language via a cross-modal projector and LLM.
  • High-level semantic states are converted into physically consistent trajectories.

Main Results:

  • VLA-MP achieved top scores on the LangAuto benchmark series (44.3, 63.5, 78.4).
  • High infraction scores (0.89-0.95) indicate safe driving performance.
  • Outperformed existing VLA methods like LMDrive and AD-H.

Conclusions:

  • VLA-MP demonstrates effective language-conditioned driving, adaptability, and safety prioritization.
  • Combining multimodal perception, language reasoning, and physics-aware adapters is promising for autonomous driving.
  • The framework offers a robust and interpretable approach to autonomous navigation.