作者 | 高毅鹏@知乎 编辑 | 自动驾驶之心 原文链接： https://zhuanlan.zhihu.com/p/1981658979764568316 点击下方 卡片 ，关注" 自动驾驶之心 "公众号 戳我-> 领取 自动驾驶近30个 方向 学习 路线 >>自动驾驶前沿信息获取 → 自动驾驶之心知识星球 本文只做学术分享，如有侵权，联系删文 华为 华为 ADS 硬件迭代：多传感器融合方案 从 ADS1.0 到 4.0 的迭代过程中，华为坚持多传感方案，通过激光雷达、毫米波雷达、摄像头等多传感器互补融合感知，达到全天候全天时的高效感知能力。 | | 激光雷达 | 毫米波雷达 | 超声波雷达 | 摄像头 | | --- | --- | --- | --- | --- | | 不息留 | | | | | | 作用 | 成像级传感器,可以渲染3D环境 | 盲点监测、变道辅助 | 5m以内的短距感知,泊车辅助 | 环境探测、障碍物信息采集 | | 优点 | 成像干净、噪点少,信息丰富 | 条件下仍可完成深刻 | 感知距离远,在夜晚以及极端天气的 获取信息较为丰富、速度快、抗电 成本相对较低;可以描绘道路环境的 磁 ...

Core Viewpoint - Mobileye, a leading global supplier of autonomous driving solutions, is entering the field of embodied intelligence by acquiring a humanoid robotics company, Mentee Robotics, for $6.3 billion [4]. Group 1: Industry Developments - The acquisition by Mobileye signifies a significant investment in humanoid robotics, highlighting the growing intersection between autonomous driving and embodied intelligence [4]. - NVIDIA has been advancing the development of embodied intelligence-related models and infrastructure, including the GR00T series models and embodied simulation frameworks [7]. - Tesla has been focusing on the development of its Optimus humanoid robot, indicating that a substantial portion of its future profits will come from this robotics business [8]. Group 2: Market Trends - Companies like Waymo are actively developing embodied intelligence technologies, and there are reports of Xiaopeng Robotics planning to achieve mass production this year [9]. - Major automotive manufacturers in China, such as Geely, BYD, SAIC, and GAC, are increasingly establishing or investing in various humanoid robotics companies [9]. - The technological similarities in perception, localization, and planning between autonomous driving and embodied intelligence suggest that cross-industry integration will become more frequent [10].

Core Insights - The article discusses the advancements in autonomous driving technology, particularly the transition from Level 2 (L2) to Level 4 (L4) automation, highlighting that high-level assisted driving has reached a "quasi-L4" stage by December 2025 [3] - It emphasizes the significant investment in the L4 sector, with over 30 billion yuan raised in the domestic autonomous driving industry in 2025, indicating a shift in focus towards L4 technology [3] - A roundtable event is planned to explore the technological and commercial realities of L4 autonomous driving, featuring insights from leading companies in the field [3] Group 1: Industry Developments - The technology pathways for L2 and L4 are converging, allowing for the reuse of the same model across both levels [3] - The article notes that the L4 sector is gaining renewed attention due to its potential to reach a critical development phase [3] Group 2: Event Details - A significant roundtable discussion on L4 autonomous driving will take place, featuring diverse perspectives from top companies in the industry [3] - The event aims to delve into the evolution of L4 technology, market dynamics, and future directions [3] Group 3: Key Speakers - He Bei, founder and chairman of Sinian Intelligent Driving, has extensive experience in autonomous driving and has published over 30 papers and holds more than 100 patents [4] - Miao Qiankun, CTO of New Stone Age Autonomous Vehicles, has over 15 years of experience in R&D and has led the development of L4 urban logistics delivery vehicles, which are operational in over 300 cities [5] - Wang Ke, VP of AI R&D at Karl Power, and Ma Qianli, Tech Lead at a top global automotive company, bring significant expertise in autonomous vehicle technology and commercial operations [6]

Core Viewpoint - Yushi Technology has received approval for its overseas listing in Hong Kong, marking a significant step in its development amidst a challenging financial landscape and a rapidly evolving autonomous driving industry [1] Group 1: Company Overview - Yushi Technology plans to issue up to 18.9142 million shares for overseas listing and convert 111 million shares held by 41 shareholders into publicly tradable shares in Hong Kong [1] - The company has accumulated losses of 784 million yuan from 2022 to the first half of 2025, with high R&D expenditures and rising raw material costs [1] - The autonomous driving industry is transitioning from demonstration testing to replicable operations, with closed or semi-closed scenarios like airport towing and logistics becoming key areas for L3-L4 level autonomous driving [1] Group 2: Market Position and Strategy - Yushi Technology is the largest supplier of L4 autonomous driving solutions in airport and factory scenarios in Greater China, holding market shares of 91.7% and 45.1% respectively [2] - The company focuses on closed scenarios, which have lower regulatory hurdles and faster implementation, but this limits its revenue scale and expansion capabilities compared to competitors focusing on open scenarios like Robotaxi [2][3] - Competitors such as Xiaoma Zhixing and Wenyuan Zhixing have adopted different strategies, focusing on public transportation needs and achieving higher revenue scales [3][4] Group 3: Financial Performance - Yushi Technology's revenue has grown rapidly over three years, but losses remain high, with revenues of 65.483 million yuan, 161 million yuan, and 265 million yuan from 2022 to 2024, and losses of 250 million yuan, 213 million yuan, and 212 million yuan in the same period [5][6] - The company's R&D expenses are significant, with expenditures of 189 million yuan, 184 million yuan, and 196 million yuan from 2022 to 2024, creating long-term financial pressure [6] - The asset-liability ratio has increased from 20.9% in 2022 to 40.2% by mid-2025, indicating rising financial risk [6] Group 4: Customer Structure and Market Dynamics - Revenue concentration among the top five customers has fluctuated, with their contributions being 57.6%, 66.0%, 46.2%, and 82.8% from 2022 to the first half of 2025 [7] - The average retention rate of key customers has decreased from 100% in 2022 to 40% in the first half of 2025, indicating potential volatility in revenue recognition [7] - The autonomous driving market is entering a phase focused on delivery, cost, and scale, with Yushi Technology facing challenges in maintaining profitability and competitive advantage [8]

Core Viewpoint - Huatai Securities initiates coverage on WeRide (WRD.US, 0800.HK) with a "Buy" rating, setting target prices at HKD 52 for Hong Kong shares and USD 20 for U.S. shares, citing the company's leading autonomous driving technology and comprehensive business development model as key strengths [1][2] Group 1: Competitive Advantages - WeRide's core competitive advantages include a globally leading scaled L4 fleet and multi-regional commercialization capabilities [1] - The company has established a full-stack self-developed software platform and automotive-grade hardware system, which solidifies the foundation for L4 scaling [1] - A diversified global ecosystem partnership has been formed, enhancing the ability to scale autonomous driving [1] Group 2: Market Position and Strategy - Huatai Securities believes that China is not merely a follower in the Robotaxi sector, as leading companies in both China and the U.S. share similar technological levels and evolution directions [1] - Chinese companies benefit from a mature vehicle and sensor supply chain, allowing for lower vehicle and kit costs, and are more proactive in applying automotive-grade chips [1] - The company is expected to achieve significant growth in overseas markets, particularly in regions like the Middle East, where it has built the largest Robotaxi fleet [2]

Core Viewpoint - The competition between Tesla and Nvidia is intensifying, with both companies aiming to dominate the autonomous driving market, leveraging their unique strengths and strategies [1][5][22]. Group 1: Company Strategies - Nvidia's Alpamayo platform aims to reshape the autonomous driving development ecosystem by providing a framework for AI reasoning, integrating visual, language, and action models [3][7][11]. - Tesla's approach relies on extensive real-world driving data, claiming that achieving safe, unsupervised autonomous driving requires approximately 100 billion miles of training data, which Tesla is already accumulating at a rapid pace [16][18]. - Nvidia's business model focuses on empowering automotive companies by offering a "teacher model" rather than directly selling autonomous driving solutions, allowing companies to create tailored models using their own data [11][26]. Group 2: Competitive Landscape - Tesla asserts that traditional automakers will take years to integrate AI and camera systems into their designs, suggesting that Nvidia's collaboration with these companies will not pose a significant threat to Tesla in the near term [14][15]. - The competition is not just about technology but also about data ownership and ecosystem control, with Tesla's data monopoly being a significant advantage over Nvidia's more open platform [24][26]. - The battle is evolving from a focus on individual vehicle intelligence to a broader competition involving data ecosystems, development paradigms, and industry alliances [26][27]. Group 3: Market Dynamics - The automotive industry's shift towards intelligent systems is characterized by a multi-dimensional competition, where both Tesla and Nvidia are vying for leadership in different aspects of autonomous driving technology [27]. - The emergence of strong competitors from China, with robust engineering backgrounds and market scales, adds another layer of complexity to the competition between Tesla and Nvidia [26].

Core Insights - The company, Shanghai Berai Intelligent Technology Co., Ltd., has raised over 1 billion yuan in cumulative financing by 2025 [1] - The company positions itself as a global leader in electric driverless mining trucks and zero-carbon mining solutions, focusing on "automation + electrification" as its core strategy [1] Business Model - The company's operations revolve around "unmanned transportation in mining," forming a flexible business model with four key components: intelligent driving, intelligent vehicles, intelligent transportation, and intelligent mining [1] - It provides comprehensive solutions ranging from autonomous driving systems, vehicle sales, transportation services, to fully unmanned mining operations [1] Project Achievements - The company has completed over 30 mining projects, covering various types of minerals and complex working conditions, achieving industry-leading total operating mileage [1] Future Plans - The company aims to deepen its full-stack technology integration and accelerate global market expansion, collaborating with industry partners to promote the evolution of mining transportation towards zero-carbon, intelligent, and efficient solutions [1]

图片由AI生成 作为英伟达在自动驾驶领域的竞争对手之一，英特尔子公司Mobileye也正将"物理AI"视为下一代核心方 向。 今年CES期间，Mobileye宣布收购人形机器人公司Mentee Robotics，引发行业关注。 根据官方信息，这次收购对价总额约为9亿美元（具体金额可能因相关调整而发生变动），其中包括约 6.12亿美元现金及至多约2620万股Mobileye A类普通股（具体股数将根据交割前Mentee期权的归属情况 做相应调整）。 Mobileye强调，上述金额并非最终金额，将根据股份购买协议条款进行调整。 该交易预计于2026年第一季度完成。 收购完成后，Mentee将作为Mobileye旗下独立事业部运营，在保持团队与业务连续性的同时，依托 Mobileye的AI训练基础设施，加速其AI软硬件能力的整合。 Mobileye首席执行官阿姆农·沙书亚（Amnon Shashua）表示，"双方的强强联合将使我们把握时代赋予 的独特机遇，在全球范围内引领物理AI技术在机器人与驾驶自动化领域的演进。" "Mobileye是一家在物理AI领域工作的AI公司。"沙书亚在CES的演讲中强调，过去Mobil ...

Core Viewpoint - The article discusses the advancements in autonomous driving technology, particularly the transition from Level 2 (L2) to Level 4 (L4), highlighting the significant investments and developments in the L4 sector within the industry [3]. Group 1: Industry Developments - By December 2025, the autonomous driving industry in China is expected to have raised over 30 billion in funding, with a focus on L4 technology [3]. - The article emphasizes that the technological pathways for L2 and L4 are converging, allowing for the reuse of the same model across both levels [3]. - A roundtable discussion on L4 autonomous driving will be held, featuring leading companies in the field to explore the balance between technological ideals and commercial realities [3]. Group 2: Key Speakers - He Bei, founder and chairman of Sinian Intelligent Driving, has a PhD from Tsinghua University and extensive experience in autonomous driving technology [4]. - Miao Qiankun, CTO of New Stone Age Autonomous Vehicles, has over 15 years of experience in R&D and has led the development of L4 urban logistics delivery vehicles, which have been deployed in over 300 cities and 10 countries, with a total of 15,000 vehicles delivered and over 60 million kilometers driven [5]. - Wang Ke, Vice President of AI R&D at Karl Power, previously led the perception tracking module at Zoox, a US autonomous driving unicorn [6]. Group 3: Event Details - The upcoming roundtable will delve into the evolution of L4 technology, market dynamics, and future development directions, promising a blend of depth and foresight [3]. - The event will feature a diverse range of perspectives from top companies in the L4 sector, indicating a significant interest in the current state and future of autonomous driving technology [3].

Core Viewpoint - The article discusses the advancements and applications of 3D Generative Systems (3DGS) in the context of autonomous driving, emphasizing the importance of scene reconstruction and generation technologies for creating realistic driving environments [1][3]. Group 1: Scene Reconstruction Work - The publication of StreetGaussian at ECCV2024 marks a significant step in the wave of autonomous driving scene reconstruction [2]. - A large-scale vehicle asset reconstruction dataset named 3DRealCar has been released [2]. - The Balanced3DGS algorithm accelerates 3DGS training by nearly eight times [2]. - The Hierarchy UGP paper is set to be presented at ICCV2025, focusing on autonomous driving scene reconstruction [2]. - StyledStreets introduces a multi-style scene generation algorithm with spatiotemporal consistency for autonomous driving [2]. Group 2: Importance of Scene Reconstruction - Traditional vehicle testing heavily relies on real-world tests, which often fail to replicate many corner cases, and there is a significant domain gap in conventional simulation environments [3]. - The high-fidelity scene reconstruction and editing capabilities of 3DGS make it possible to address these challenges [3]. - The development trajectory of 3DGS is clear: static reconstruction → dynamic reconstruction → hybrid reconstruction → feed-forward GS, with applications extending beyond autonomous driving to 3D fields, embodied intelligence, and the gaming industry [3]. Group 3: 3DGS Learning Path - A comprehensive learning roadmap for 3DGS has been developed, covering point cloud processing, deep learning theories, real-time rendering, and practical coding [5]. - The course titled "3DGS Theory and Algorithm Practical Tutorial" aims to provide a structured approach to mastering the 3DGS technology stack [5]. Group 4: Course Structure - The course consists of six chapters, starting with foundational knowledge in computer graphics and progressing through principles, algorithms, and specific applications in autonomous driving [10][11][12][13][14]. - Each chapter includes practical assignments and discussions on important research directions and industry applications [13][14][15]. Group 5: Target Audience and Outcomes - The course is designed for individuals with a background in computer graphics, visual reconstruction, and programming, aiming to equip them with comprehensive knowledge and skills in 3DGS [19]. - Participants will gain insights into industry demands, pain points, and opportunities for further engagement with academic and industrial peers [15][19].