2025年12月10日，全球图像级激光雷达解决方案提供商图达通正式登陆香港交易所主板。作为港交所第 三家以De-SPAC模式成功上市的企业，图达通与已上市的禾赛科技、速腾聚创齐聚资本市场，标志着国 产激光雷达"三强"格局在资本层面正式成型。 上市首日，图达通开盘股价上涨超10%，市值升至150亿港元以上。这一积极的市场反应，为其作为上 市公司的全新发展阶段揭开了序幕。 市场对其的看好并不奇怪，作为目前激光雷达领域的头部玩家之一，图达通不仅成长性凸显，而且已经 具备出众的自我造血能力，这也是其能够在激烈的竞争中率先突围的关键。 放眼更长远的未来，图达通上市之后值得期待吗？ 01 双线破局：构筑量产时代的核心壁垒 上市其实是一次严选，是对企业战略路径、商业模式、财务健康性、市场竞争优势、所处赛道前景的一 次全方位检阅。 公开资料显示，图达通是全球首家实现车规级高性能激光雷达解决方案量产的供应商。业绩表现上， 2022年至2024年，其ADAS激光雷达解决方案累计销售收入全球排名第二；2024年全年交付约23万台车 规级激光雷达，凭借硬核产品实力与规模化交付能力，与华为、禾赛科技、速腾聚创共同跻身ADAS激 光雷达 ...

今年 9 月，他曾表示："年底前应该不再需要安全驾驶员。"在 10 月的第三季度财报电话会议上，他进一步说明："我们预计在今年年底前，在奥斯汀至少大 部分区域将不再配备安全驾驶员。" 到了 11 月，他在股东大会的公开声明中再次强调了这一时间表："我预计今年 Robotaxi 将在奥斯汀的大片区域实现无安全驾驶员运营。" 目前，特斯拉在奥斯汀的本地道路测试中，安全监督员通常坐在副驾驶座位；而在高速公路路段，则坐在驾驶座。相比之下，在湾区（Bay Area）的网约车 运营中，驾驶座始终配备一名安全监督员。 若按三周后的时间点计算，特斯拉将刚好在 12 月 31 日前两天兑现其"年底前实现无人监督运营"的承诺。尽管时间紧迫，但这一进展将是 Robotaxi 项目的 重要飞跃，并有望有力回击众多质疑者，他们一直认为当前版本的 Robotaxi 与普通网约车（如 Uber）并无实质区别。 此外，特斯拉今年已扩大其 Robotaxi 车队规模，但尚未公布具体数量。 IT之家 12 月 10 日消息，特斯拉首席执行官埃隆・马斯克（Elon Musk）周二在 xAI 黑客马拉松活动上确认，公司将在三周内从奥斯汀（Austi ...

Core Viewpoint - DNP has developed a technology that could reduce energy consumption in advanced semiconductor manufacturing by 90%, significantly lowering the production costs of AI chips [2]. Group 1: Technology Development - DNP plans to start mass production of a template material for manufacturing cutting-edge 1.4 nm chips by 2027 [2]. - The current manufacturing of such advanced chips requires EUV lithography equipment, which is exclusively produced by ASML Holding [2]. - Lithography processes account for 30% to 50% of the total cost of chip manufacturing, with smaller circuit sizes leading to increased power consumption [2]. Group 2: Market Dynamics - Canon has begun selling semiconductor lithography machines in 2023, which consume less power than EUV equipment, with an estimated price of several billion yen (approximately 6.4 million USD) [2]. - The introduction of nanoimprint lithography technology could face challenges in large-scale production due to the need for high economic efficiency [3]. - Major companies like Samsung and TSMC plan to start mass production of 1.4 nm chips in 2027 and 2028, respectively, and are interested in nanoimprint lithography technology [3]. Group 3: Industry Opportunities - If the nanoimprint market expands, it could create opportunities for material manufacturers like DNP [4]. - Fujifilm Holdings has announced plans to enter the market by producing materials for circuit formation on wafers [4]. - Canon is set to deliver its first nanoimprint lithography equipment to the Texas Instruments research institute in 2024 [4].

Core Insights - Waymo is advancing its autonomous driving technology by prioritizing "verifiable safe AI" as a core principle, significantly reducing the accident rate compared to human drivers by over ten times [2][5][19] - The company has achieved over 100 million miles of fully autonomous driving, continuously improving road safety in its operational areas [2][5] Group 1: Waymo's AI Strategy - Waymo's AI ecosystem integrates a driver, a simulator, and an evaluator, all powered by the Waymo Foundation Model, ensuring safety is a foundational element rather than an afterthought [5][12] - The Waymo Foundation Model serves as a multifunctional "world model," providing a robust interface for interaction among various components and supporting end-to-end signal backpropagation during training [8][10] Group 2: Components of the AI Ecosystem - The driver model generates safe and compliant action sequences, with its capabilities distilled into more efficient student models for real-time deployment in vehicles [14] - The simulator creates high-fidelity virtual environments for testing the driver model under diverse and challenging scenarios, while the evaluator analyzes driving behavior to provide feedback for continuous improvement [14][15] Group 3: Learning and Optimization Mechanisms - Waymo employs a dual learning loop: an internal loop driven by the simulator and evaluator for reinforcement learning, and an external loop utilizing real-world driving data to enhance the driver model [17][19] - The company has amassed a vast amount of fully autonomous driving data, which is crucial for training and optimizing its systems, surpassing the reliance on human driving data [19]

Group 1: Sam's Club Incident - A consumer reported finding a live mouse in a delivery bag containing a box of 24 pieces of Member's Mark mochi purchased via the Sam's Club app [3][5] - Sam's Club stated that the initial investigation indicated the mouse likely entered the bag during the time it was left at the pickup point, which is outdoors and surrounded by vegetation [5] - The company has apologized for the incident and committed to improving packaging management and delivery services [5] Group 2: Market Movements - Bubble Mart's stock price has dropped nearly 44% over four months, resulting in a market value loss exceeding 200 billion HKD (approximately 180 billion RMB) [12][13] - Deutsche Bank's report indicated that Bubble Mart plans to significantly increase the production of its Labubu toys from 10 million units in the first half of the year to an average of 50 million units per month by the end of the year, warning that mass production could signal a decline in brand popularity [13] Group 3: Regulatory Changes - China will ban the production of mercury-containing thermometers and blood pressure monitors starting January 1, 2026, due to safety concerns regarding mercury's toxicity [6][7] Group 4: Corporate Developments - SpaceX is reportedly planning an IPO in 2026, aiming to raise over 30 billion USD with a target valuation of approximately 1.5 trillion USD [7] - Xiaomi has initiated personnel adjustments in its China operations, affecting key roles in mobile phones, automotive, and major appliances, in response to recent performance pressures [16]

以下文章来源于RoboX ，作者RoboX RoboX . 从AI汽车到机器人，我们关注最具潜力的超级智能体！ 作者 | RoboX 来源 | RoboX 原文链接： 地平线苏箐演讲全文提炼：自动驾驶的曙光、痛苦与轮回 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 演讲者：苏箐 | 地平线副总裁&首席架构师 演讲时间 ：2025.12.9 演讲场合 ：2025地平线技术生态大会 全文提炼如下： 今年，我们确实能看到自动驾驶的技术路径是比较清晰的，但也会看到有更难的问题在前面。你知道这些问题能解掉，但应该怎么解今天还不知道。 绝大多数行业外的人，可能并不理解自动驾驶团队面临的困难和压力。这种智力和体力的双重压榨极度痛苦，因为有SOP的时间压在那儿，然后又有方法论的变化， 还有各种corner case需要去解。 在稠密的世界里连续运行的时候，所有的case都需要解决，这就是这个行业非常痛苦的地方。 曙光：重大分水岭的出现 我刚准备加入地平线的时候，和余凯博士聊过几次， ...

华见早安之声 请各位听众升级为见闻最新版APP，以便成功收听以下音频。 李强同主要国际经济组织负责人举行"1+10"对话会，表示开放合作是落实全球治理倡议的重要路径，应当加大市场相互开放，避免经贸问题泛 政治化、泛安全化。 市场概述 美联储重磅决议公布前夕，美股涨势不振，标普道指两连跌至一周低位，纳指小幅反弹，意外回暖。摩根大通跌近5%、领跌道指成分股。芯片指数止步两 连涨，英伟达回落0.3%，而博通涨超1%，三连涨、创历史新高；苹果五连跌，而特斯拉和谷歌反弹超1%。 美联储关注的就业指标意外回暖，美国职位空缺不降反升，美债价格止涨回跌、收益率盘中V形拉升，十年期美债收益率逼近两个多月高位。美国职位空缺 公布后，美元指数加速涨至一周来高位，加密货币拉升，比特币盘中涨超5%、涨破9.4万美元，以太坊一度涨近10%。 原油两连跌至两周低位，盘中跌超1%。黄金盘中创一周新低后转涨，期金收盘告别本月内低位；白银反弹、史上首次涨破60美元，期银一度涨近5%。伦铜 跌超1%，告别纪录高位。 亚洲时段，A股震荡分化，算力领涨，光伏拉升，商业航天继续活跃，中际旭创新高，港股走低，恒指和科指跌超1%，有色金属、半导体走弱。 要 ...

ADAS 激光雷达产品维持增长。2025 年第三季度，公司激光雷达产品销量为18.56 万台，同比增长 34%；其中用于ADAS 应用的激光雷达产品销量为15 万台，同比增长14.3%。截至2025 年9 月30 日，公 司已成功取得31 家汽车整车厂及一级供应商车型量产定点订单已增加至134 款，并为其中15 家汽车整 车厂及一级供应商的47 款车型实现SOP。 自动驾驶Robotaxi 业务持续推进。公司与滴滴自动驾驶达成重要合作，其L4 级Robotaxi 将配备10 颗 RoboSense 车规级数字化激光雷达。在2025 上海车展期间，小马智行宣布其第七代Robotaxi 将搭载4颗 RoboSense 车规级全固态数字激光雷达。全球多家主要的Robotaxi及Robotruck 企业已与速腾签署正式量 产合作协议，包括滴滴自动驾驶、百度、小马智行、文远知行以及多家美国的领先L4 级自动驾驶公司 等知名行业品牌。 机构：甬兴证券 研究员：应豪 事件描述 速腾聚创于11 月25 日公告公司第三季度业绩。公司前三季度收入同比增长1.24%至11.9 亿元，净亏损 2.52 亿元。 核心观点 投资建议 我 ...

Core Viewpoint - The article introduces a new course titled "World Models and Autonomous Driving Small Class," focusing on advanced algorithms in the field of autonomous driving, including general world models, video generation, and OCC generation [1][3]. Course Overview - The course is developed in collaboration with industry leaders and follows the success of a previous course on end-to-end and VLA autonomous driving [1]. - The course aims to enhance understanding and practical skills in world models, which are crucial for the advancement of autonomous driving technology [11]. Course Structure Chapter 1: Introduction to World Models - This chapter covers the relationship between world models and end-to-end autonomous driving, the history of world models, and current application cases [6]. - It discusses various types of world models, including pure simulation, simulation plus planning, and generating sensor inputs and perception results [6]. Chapter 2: Background Knowledge of World Models - The second chapter focuses on foundational knowledge related to world models, including scene representation, Transformer technology, and BEV perception [6][12]. - It highlights key technical terms frequently encountered in job interviews related to world models [7]. Chapter 3: Discussion on General World Models - This chapter addresses popular general world models and recent trends in autonomous driving jobs, including models from Li Feifei's team and DeepMind [7]. - It provides insights into the core technologies and design philosophies behind these models [7]. Chapter 4: Video Generation-Based World Models - The fourth chapter focuses on video generation algorithms, showcasing significant works such as GAIA-1 & GAIA-2 and recent advancements from various institutions [8]. - It includes practical applications using open-source projects like OpenDWM [8]. Chapter 5: OCC-Based World Models - This chapter explores OCC generation algorithms, discussing three major papers and a practical project that extends to vehicle trajectory planning [9]. Chapter 6: World Model Job Topics - The final chapter shares practical experiences from the instructor's career, addressing industry applications, pain points, and interview preparation for related positions [10]. Target Audience and Learning Outcomes - The course is designed for individuals aiming to deepen their understanding of end-to-end autonomous driving and world models [11]. - Upon completion, participants are expected to achieve a level equivalent to one year of experience as a world model autonomous driving algorithm engineer, mastering key technologies and being able to apply learned concepts in projects [14].

Core Insights - The article emphasizes the importance of automated 4D annotation data in enhancing autonomous driving capabilities, driven by the need for complex training data formats [2][4] - It highlights the challenges faced in automated annotation, including sensor calibration, occlusion handling, and quality control of annotations [4][9] Group 1: Automated 4D Annotation - The backbone of autonomous driving capabilities is the vast training data generated through automated 4D annotation, which is increasingly complex compared to traditional methods [2] - The shift towards end-to-end data requires synchronized sensor annotations of dynamic and static elements, ensuring the completeness of training data [2][4] Group 2: Challenges in Automated Annotation - Key challenges in the industry include calibrating and synchronizing different sensors, managing occlusion issues, and ensuring the generalization of algorithms [4] - The need for high-quality annotation results and effective automated quality checks are critical pain points in the current landscape [4] Group 3: Educational Initiatives - The article introduces a course focused on automated 4D annotation algorithms, aimed at addressing the industry's needs and enhancing algorithmic capabilities [4][8] - The course covers the entire process of dynamic and static object annotation, including practical exercises to reinforce learning [8]