世界模型能够从根本上解决VLA系统对数据的依赖，是伪命题...

Core Viewpoint - The article discusses the ongoing debate between two approaches in the autonomous driving sector: VLA (Vision-Language Action) and WA (World Model), highlighting that both are fundamentally reliant on data, but differ in their methodologies and implications for the future of autonomous driving [1][2]. Summary by Sections VLA vs. WA - The autonomous driving landscape is splitting into two camps by 2025: companies like Xiaopeng, Li Auto, and Yuanrong Qixing are betting on the VLA approach, while Huawei and NIO are advocating for the WA model [1]. - WA is claimed to be the ultimate solution for achieving true autonomous driving, but the article argues that it is merely a rebranding of data dependency [1]. Data Dependency - Both VLA and WA are based on the premise that "data determines the upper limit" of capabilities [2]. - VLA relies on real-world multimodal data to train reasoning abilities, while WA requires a combination of real data and simulated data to enhance its capabilities [2]. - The industry is confused about the distinction between "data form" and "data essence," leading to misconceptions about the reliance on data [2]. Industry Misconceptions - The article emphasizes that the discussion should not focus on whether data is needed, but rather on how to efficiently utilize data [2]. - VLA and WA represent different methods of data collection and usage, with data remaining the core competitive advantage in autonomous driving until true artificial intelligence is realized [2]. Community and Resources - The "Autonomous Driving Knowledge Planet" community has over 4,000 members and aims to grow to nearly 10,000 in two years, providing a platform for technical exchange and sharing of knowledge in the autonomous driving field [4][10]. - The community offers resources such as learning routes, technical discussions, and access to industry experts, facilitating knowledge sharing among newcomers and advanced practitioners [4][11].