视觉语言行动模型(VLA)

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三问三解 | VLA
Zhong Guo Zhi Liang Xin Wen Wang· 2025-05-15 07:56
Core Insights - The evolution of autonomous driving technology has progressed from rule-based systems to Vision-Language-Action (VLA) models, marking significant advancements in AI applications [1][2]. Group 1: VLA Model Overview - VLA (Vision-Language-Action Model) integrates visual, language, and action capabilities into a single model, enabling end-to-end mapping for action execution based on input [2]. - The VLA model consists of several key modules: visual encoder, language encoder, cross-modal fusion module, and action generation module, facilitating high-level feature extraction and decision-making [4]. - Core features of VLA include multi-modal perception and decision-making, global context understanding, and system transparency, allowing for real-time perception and human-like reasoning [4]. Group 2: VLA Capabilities - VLA can handle complex driving scenarios by understanding both the physical world and its operational logic, surpassing previous models like VLM [9]. - With access to vast amounts of quality data, VLA models can achieve driving performance close to human levels, with potential to exceed human driving capabilities in fully autonomous scenarios [9]. Group 3: World Model Integration - The World Model constructs a virtual environment to simulate and predict real-world traffic scenarios, enhancing the VLA model's understanding of complex situations [10][12]. - It provides richer contextual information for VLA, aids in simulated training, and validates safety through extreme scenario testing [12]. Group 4: Future Developments - The training and deployment of VLA models face significant computational challenges, but advancements in distributed training technologies are expected to improve efficiency [12].