智能驾驶商业化的竞争残酷性在于，先出发的第一名优势未必能长久稳固。 今年 2 月，比亚迪一口气发布了 21 款智驾版车型，58 岁的掌门人王传福一改此前 "无人驾驶是扯淡" 的论断，公开称 "下半场是智能化"。凭借年销数百万辆车的规模，比亚迪直接催化 "全民智驾" 这场 热潮。 规模普及还未结束，智驾又继续被 AI 大模型驱动。理想、小鹏汽车一号位大谈 AI，智驾技术路线从 端到端，切换到 VLA、世界模型， 不断突破软件上限 。 特斯拉 "图像输入，控制输出" 的端到端方案，也让智能驾驶一步步向 L4 级自动驾驶逼近。国内互联 网公司已经下场布局，抢夺 Robotaxi 出行口入：小鹏的 Robotaxi 车型将接入高德平台；哈罗、宁德 时代与蚂蚁集团将联合推出前装量产 Robotaxi；京东注册了 Robotaxi 相关商标。 汽车 AI 芯片作为承载软件模型、决定体验上限的硬件底座，战略价值愈发凸显。特斯拉 FSD 搭载的 新一代芯片 AI5 还未投入量产，但性能更强的 AI6、AI7 研发计划就已经列在马斯克案头。 国内，尽管市场上英伟达、华为的智驾芯片已占据车企主流视野，但技术路线的剧烈变迁、车企对 ...

Core Insights - The global automotive AI chip market is projected to grow from $13.8 billion in 2024 to $34.3 billion by 2029, with a compound annual growth rate (CAGR) of 20.45% [2] - AI chips are becoming the central component for enabling key applications such as autonomous driving, smart cockpits, and predictive maintenance in the automotive industry [3][4] - The market is driven by advancements in technology, increasing efficiency of AI algorithms, and stricter regulations on ADAS and active safety features [4] Market Dynamics - The automotive AI chip market is expanding with applications ranging from in-vehicle smart functions to platforms for intelligent perception, decision-making, and control [3] - Major drivers include the rising penetration of autonomous driving, the complexity of ADAS systems, and the demand for AI processing capabilities in smart cockpits [4] - The shift from general-purpose AI chips to automotive-grade AI chips is evident, with a focus on low latency and low power consumption [4] Competitive Landscape - The competition in the automotive AI chip market is becoming increasingly differentiated, with companies like NVIDIA and Qualcomm holding significant market shares [5] - NVIDIA's Orin chip has been installed in over 5 million vehicles, while Qualcomm's SA8155P chip has a 40% penetration rate in high-end models [5] Technological Advancements - The computational density of AI chips is continuously improving, with expectations for single-chip performance to reach 2000 TOPS in the coming years [6] - The rise of integrated storage-compute architectures is breaking traditional bottlenecks, enhancing data throughput and energy efficiency [6] Industry Trends - Edge computing and cloud collaboration are emerging as key trends in the development of automotive AI chips, enabling real-time decision-making and efficient data flow [7] - The market is witnessing a shift from traditional hardware sales to "Compute as a Service" (CaaS) models, providing flexible service options for users [8] Strategic Directions - Companies are advised to establish a "general-purpose computing platform + dedicated acceleration module" approach to enhance computational efficiency and adaptability [9] - Building a closed-loop ecosystem of "chip-algorithm-data" is crucial for rapid technological iteration and optimization [9] Future Outlook - The development of automotive AI chips is not only a race of technological iteration but also a transformation of industrial ecosystems and business models [10] - As chips become the "digital engine" of vehicles, the entire industry stands at a pivotal point of transformation towards smart automotive solutions [10]