Core Viewpoint - The emergence of Vision-Language-Action (VLA) models is set to revolutionize the intelligent assisted driving industry, moving from traditional modular systems to a more integrated end-to-end architecture, enhancing driving experience and capabilities [1][2][3]. Industry Trends - The intelligent assisted driving sector is witnessing a shift from "usable" to "user-friendly" experiences, driven by the increasing adoption of new energy vehicles and the demand for improved driving assistance [3]. - VLA models are expected to dominate the market, with projections indicating that by 2030, VLA-driven end-to-end solutions could capture 60% of the L4 market share, leading to a reevaluation of the value chain for traditional Tier 1 suppliers [4]. Technological Advancements - The VLA model integrates visual, language understanding, and action decision-making, significantly enhancing scene reasoning and generalization capabilities compared to previous models [2][3]. - The VLA architecture is seen as a more comprehensive evolution of the end-to-end and VLM (Vision-Language Model) combination, addressing limitations in complex driving scenarios [3]. Competitive Landscape - Tesla is positioned as a potential beneficiary of this transformation, with its FSD Beta V12 showing a 76% reduction in intervention frequency compared to the previous version [4]. - Domestic automakers are also actively exploring VLA technologies, with companies like Li Auto emphasizing the importance of VLA in their future models [4]. Manufacturing Innovations - AI is driving a paradigm shift in automotive manufacturing, moving from traditional assembly line methods to more efficient, data-driven "smart island" models [2][5]. - The integration of AI in manufacturing processes is seen as essential for overcoming challenges such as long changeover times and quality fluctuations [6][7]. Future Outlook - The VLA technology is expected to redefine the competitive landscape of the intelligent assisted driving market, leading to a layered market structure rather than a single dominant technology [6]. - The acceptance of AI for process optimization in manufacturing is growing, with companies recognizing the need for comprehensive AI integration to enhance operational efficiency [8].