VLA-MP: A Vision-Language-Action Framework for Multimodal Perception and Physics-Constrained Action Generation in Autonomous Driving
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View abstract on 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.
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.
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