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: the VLA (Vision-Language Action) route favored by companies like Xiaopeng, Li Auto, and Yuanrong Qixing, and the World Model (WA) approach promoted by Huawei and NIO. It argues that the WA approach is fundamentally flawed as it relies heavily on data, which is a critical asset in the industry [2][3]. Summary by Sections VLA vs. WA - The VLA approach leverages vast amounts of real-world data to enhance reasoning capabilities, while the WA model seeks to reduce reliance on real data by using simulated data to expand its capabilities. However, the article posits that both approaches are fundamentally about how data is utilized rather than whether data is necessary [2][3]. Data Dependency - Both VLA and WA are built on the premise that "data determines the ceiling" of capabilities. VLA relies on multi-modal data from real scenarios, while WA requires a combination of real and simulated data to enhance its generalization ability. The industry often confuses the "form of data" with its "essence," leading to misconceptions about the role of data in autonomous driving [3]. Industry Insights - The article emphasizes that the real challenge is not whether to depend on data, but how to efficiently utilize it. It highlights that before true artificial intelligence is realized, data will remain the core competitive advantage in the autonomous driving industry [3]. Community and Learning Resources - The article promotes a community platform for knowledge sharing among industry professionals and academics, offering resources such as learning routes, technical discussions, and job opportunities in the autonomous driving field [8][9][18]. Technical Learning and Development - The community provides a comprehensive set of learning materials covering over 40 technical directions in autonomous driving, including VLA, multi-modal models, and various simulation tools, aimed at both beginners and advanced practitioners [19][39]. Networking Opportunities - The platform facilitates networking opportunities with industry leaders and experts, allowing members to engage in discussions about trends, technologies, and career development in the autonomous driving sector [22][92].