Investment Rating - The report maintains a "Positive" investment rating for the software and services industry [11] Core Insights - The global automotive industry is undergoing a deep transformation, shifting from traditional mechanization to highly intelligent and connected driving technologies, which are becoming the main driving force for innovation in the automotive sector [4][7] - The "Vehicle-Road-Cloud Integration" construction, as a leading intelligent driving solution in China, is expected to enter a new construction cycle supported by policies, accelerating the development of related industrial chains [10][9] - In the short term, roadside construction is expected to accelerate, leading to a gradual increase in vehicle-mounted terminals, while in the medium to long term, operational services will have vast market potential [10][4] Summary by Sections Intelligent Driving: A New Trend in Future Mobility - Intelligent driving technology is transitioning from L2 (combined driving assistance) to L3 (conditional automated driving), with the penetration rate of high-level intelligent driving systems in China's passenger car market continuously increasing [7][23] - In 2023, the sales of intelligent connected passenger vehicles with combined auxiliary driving functions reached 9.953 million units, with a market penetration rate of 47.3% [23] Vehicle-Road-Cloud Integration: China's Leading Intelligent Driving Solution - Intelligent driving is divided into two technical paths: vehicle-road-cloud integration and single-vehicle intelligence. The former combines vehicles with road and cloud resources for safer and more efficient automated driving [8][40] - The domestic market for intelligent driving SoC chips is currently at a disadvantage, with foreign solutions dominating over 80% of the market share [44][45] Policy Support: Accelerating Vehicle-Road-Cloud Integration Construction - Policy guidance is a key driving force for the development of vehicle-road-cloud integration in China, with the government continuously issuing relevant policies to promote the digital upgrade of transportation facilities [9][56] - As of July 2024, 17 national-level intelligent connected vehicle testing zones and 20 pilot cities for vehicle-road-cloud integration have been established [57][61] Investment Recommendations: Focus on the Entire Vehicle-Road-Cloud Industry Chain - The report suggests focusing on the entire industry chain of vehicle-road-cloud integration, particularly on infrastructure manufacturers that facilitate vehicle-road collaboration [10][4]

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]

Core Insights - The penetration rate of passenger car driving domain controllers is projected to reach 31% by June 2025, with Nvidia's chip share increasing to 53.5%, marking a year-on-year growth of 25.4% [1][3] - The overall market for L2 and above autonomous driving features in passenger vehicles has increased by 13 percentage points year-on-year, reaching a penetration rate of 29.7% by June 2025 [4] Industry News - Waymo has received the first autonomous vehicle testing license in New York City [2] - The Shangjie H5 has launched with the HUAWEI ADS4 driver assistance system [2] - Horizon Robotics has upgraded its HSD, with the first model being the Chery Xingtu E05 [2] - Pony.ai has officially launched autonomous driving services in Shanghai's Pudong district [2] - WeRide has released an end-to-end driver assistance solution expected to be mass-produced by 2025 [2] - Hesai Technology has secured a contract for laser radar with a brand under Toyota, with mass production starting in 2026 [2] - The new generation of the IM LS6 will feature the Supcon Juchuang 520-line laser radar [2] High-Frequency Data Updates - The penetration rate of 8-megapixel cameras in passenger vehicles has reached 39.7%, a year-on-year increase of 22 percentage points [3] - The market share of laser radar in passenger vehicles has risen to 10%, with Huawei leading at 47% market share [3] Sensor and Control Unit Penetration - By June 2025, the penetration rates for front-view cameras, millimeter-wave radars, and laser radars are 67.6%, 57.4%, and 9.7% respectively, with year-on-year increases of 4, 5, and 2 percentage points [4] - The penetration rate of autonomous driving domain controllers in passenger vehicles is 30.9%, reflecting a year-on-year increase of 13.1 percentage points [4] Smart Cabin and Connectivity - The penetration rates for 10-inch and above central screens, 10-inch and above LCD instrument panels, HUDs, and smart cabin domain controllers are 85.2%, 50%, 19.4%, and 40.8% respectively, with varying year-on-year changes [4] - The penetration rates for OTA and T-BOX technologies are 76.8% and 69.0%, with year-on-year changes of +2 and -7 percentage points [4] Investment Recommendations - Recommended companies for complete vehicles include XPeng Motors, Leap Motor, and Geely [5] - For L4 autonomous driving, recommended companies are Pony.ai and WeRide [5] - Component recommendations include Supcon Juchuang and Hesai Technology for data acquisition, Hu Guang Co. for data transmission, and Horizon Robotics, Black Sesame, Kobot, Huayang Group, and Junsheng Electronics for data processing [5]

Core Viewpoint - Pure GPU can achieve basic functions for low-level autonomous driving but has significant shortcomings in processing speed and energy consumption, making it unsuitable for higher-level autonomous driving needs [4][40]. Group 1: GPU Limitations - Pure GPU can handle certain parallel computing tasks required for autonomous driving, such as sensor data fusion and image recognition, but is primarily designed for graphics rendering, leading to limitations [4][6]. - Early autonomous driving tests using pure GPU solutions, like the NVIDIA GTX 1080, showed a detection delay of approximately 80 milliseconds, which poses safety risks at high speeds [5]. - The data processing capacity for L4 autonomous vehicles generates about 5-10GB of data per second, requiring multiple GPUs to work together, which increases power consumption significantly [6][9]. Group 2: NPU and TPU Advantages - NPU is specifically designed for neural network computations, featuring a large number of MAC (Multiply-Accumulate) units, which optimize matrix multiplication and accumulation operations [10][19]. - TPU, developed by Google, utilizes a pulsed array architecture that enhances data reuse and reduces external memory access, achieving higher efficiency in large matrix operations compared to GPU [12][19]. - NPU and TPU architectures are more efficient for neural network inference, with NPU showing a significant reduction in energy consumption compared to GPU [36][40]. Group 3: Cost and Efficiency Comparison - In terms of energy efficiency, NPU's performance is 2.5 to 5 times better than that of GPU, with lower power consumption for equivalent AI computing power [36][40]. - The cost of NPU solutions is significantly lower than pure GPU solutions, with NPU hardware costs being only 12.5% to 40% of those for pure GPU setups [37][40]. - For example, achieving 144 TOPS of AI computing power with a pure GPU solution requires multiple GPUs, leading to a total cost of around $4000, while a solution with NPU can cost about $500 [37][40]. Group 4: Hybrid Solutions - NVIDIA's Thor chip integrates both GPU and NPU to leverage their strengths, allowing for efficient task division and compatibility with existing software, thus reducing development time and costs [33][40]. - The collaboration between GPU and NPU in autonomous driving systems enhances overall efficiency by avoiding frequent data transfers between different chips, resulting in a 40% efficiency improvement [33][40]. - The future trend in autonomous driving technology is expected to favor hybrid solutions that combine NPU and GPU capabilities to meet the demands of high-level autonomous driving while maintaining cost-effectiveness [40].