蔚来任少卿：世界模型解决的是时空认知，VLA做不到。

Core Viewpoint - The article discusses the importance of world models in intelligent driving, emphasizing that true understanding of the environment requires a high-bandwidth cognitive system that goes beyond language models [2][3][5]. Summary by Sections World Model vs. Language Model - The world model focuses on spatiotemporal cognition, while the language model addresses conceptual cognition. Language models have low bandwidth and sparsity, making them ineffective for modeling the real world's four-dimensional space-time [2][3]. - The world model aims to establish capabilities directly at the video level rather than converting information into language first [3][5]. VLA and WA - VLA (Vision-Language Architecture) is essentially an extension of language models, adding new modalities but still rooted in language. In contrast, the world model is not merely an addition of language but a comprehensive cognitive system [3][5]. - The ultimate goal of autonomous driving is to achieve open-set interactions, allowing users to express commands freely without being limited to a fixed set of instructions [3][4]. Importance of Language - Language remains crucial for three main reasons: 1. Incorporation of physical laws such as gravity and inertia into the model [6]. 2. Ability to understand and predict object movements in three-dimensional space over time [6]. 3. The vast amount of data absorbed by language models from the internet aids in training autonomous driving systems [7]. Industry Trends - The autonomous driving industry is experiencing intense competition, with many professionals considering transitioning to other fields. The ongoing debate between VLA and WA represents a larger industry transformation [9]. - The article suggests that those who remain in the industry must be versatile talents with rich technical backgrounds, as the market is expected to undergo significant changes [9]. Community and Learning Resources - A community platform has been established to provide resources for learning and sharing knowledge about autonomous driving, including video tutorials, technical discussions, and job opportunities [11][12][24]. - The community aims to gather individuals from various academic and industrial backgrounds to foster collaboration and knowledge sharing [25].