安全与体验双升级 重构汽车价值链交互逻辑 技术落地多点突破 算力、模型、场景全面赋能 在技术应用与产业实践环节，论坛嘉宾们结合企业案例，展示了AI在汽车研发、生产、产品等全链条 的落地成果。百度智能云副总裁、汽车业务部总经理高果荣详细介绍了百度在VLA（多模态智能驾 驶）领域的技术布局：依托百度百舸5.0平台与昆仑芯超节点，实现VLA多模态训练效率大幅提升；通 过45万公里高精地图与数据合成技术，显著降低数据标注成本；借助文心大模型与全流程数据闭环工具 链，构建从生成到仿真的一体化支持体系。"我们将以更高效的算力、更丰富的数据、更先进的模型与 工具，与行业伙伴共同加速智能辅助驾驶技术的跃升。"高果荣表示。 吉利控股集团首席数字官姚滨晖，从三大维度解析AI价值。在产品智能层面，聚焦辅助驾驶、智能座 舱等核心场景，提升车辆智能化体验；在企业智能层面，将AI应用贯穿研产供销服全业务链及战略、 人力等中后台环节，优化经营效率；在行业智能层面，通过旗下工业互联网平台"广域铭岛"，将内部验 证成熟的AI实践对外输出，赋能全行业智能化升级。 针对当前AI应用中的痛点，长城汽车（601633）技术中心副总经理荣雪东坦言，车企 ...

Core Insights - The forum highlighted the theme of "Car-Cloud Collaboration Driving the Leap in Intelligent Assisted Driving Technology," emphasizing the role of AI and cloud computing in the automotive industry [1] - Experts agreed that AI is driving a deep restructuring of the industrial value chain, from reshaping smart cockpit experiences to enhancing efficiency across the entire R&D, production, and marketing chain [1] Group 1: Strategic Integration of AI in Automotive - The deep integration of AI with the automotive industry is becoming a key driver of industry transformation, enhancing China's global competitiveness in the automotive sector [2] - Three integration strategies were proposed: 1. Car-cloud collaboration as the core path for AI and automotive integration, expanding new service segments including data, computing power, models, and simulations [2] 2. The automotive industry should enhance its understanding and application of AI technologies, particularly in intelligent driving, necessitating a reassessment of technology strategies [2] 3. Automotive companies should accelerate their transformation into AI-driven tech companies, capable of developing and producing various intelligent terminal products [2] Group 2: Trends in Competition and Data Utilization - Competition is shifting from single-point technology comparisons to "system efficiency + ecological collaboration," requiring the integration of internal and external resources to enhance user experience [3] - Data has evolved from being an "important resource" to a "core competitive advantage," with computing power being essential for unlocking data value, indicating a sustained high growth in data reliance and computing needs over the next two years [3] Group 3: AI Empowerment in R&D and Industry Applications - AI is driving industry implementation from point solutions to comprehensive applications, with advancements in multi-modal training and significant improvements in training efficiency through platforms like Baidu's [4] - The use of high-precision maps and data synthesis technology has significantly reduced labeling costs and improved efficiency [4] - Baidu's integration of large models and complete data closed-loop toolchains supports a seamless transition from generation to simulation [4] Group 4: AI Value Dimensions - AI's value can be categorized into three dimensions: 1. Product intelligence, enhancing vehicle smart features like assisted driving and smart cockpits [5] 2. Enterprise intelligence, covering all business activities related to company operations, including management and support functions [5] 3. Industry intelligence, leveraging AI practices to empower the entire industry through commercialized outputs [5] Group 5: Challenges and Future Directions - Current AI applications in R&D face challenges such as "tool silos," "data breakpoints," and "disconnected processes," limiting their effectiveness [6] - Future efforts will focus on transitioning from "technology-driven" to "business value-driven" approaches, integrating AI with simulation to enhance design iterations [6] - AI must evolve from being an optional enhancement to an indispensable asset in the automotive industry [6] Group 6: Safety and User Experience Transformation - AI is not only enhancing R&D but also transforming automotive safety systems and user experiences, with companies addressing regulatory compliance and cybersecurity challenges [7] - The establishment of vehicle security operation centers and AI-enabled log analysis has significantly improved alert processing efficiency [7] - The evolution of in-car voice interaction is moving towards an end-to-end processing model, enhancing the naturalness and efficiency of user interactions [7] Group 7: Implementation Framework for AI in Automotive - The integration of AI and the automotive industry is essential for industry development, relying on the establishment of car-cloud collaboration mechanisms, deep application of AI technologies, and the technological transformation of automotive companies [8][9]