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， ...

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 论文作者 | Haicheng Liao等 编辑 | 自动驾驶之心 在自动驾驶的交互场景中，最尴尬的时刻莫过于此： 乘客指着前方复杂的路口说："跟着那辆SUV"。自动驾驶系统看着眼前三辆长得差不多的车，内心OS："哪辆？是左边那辆？还是正在变道那辆？" 现有的自动驾驶视觉定位（Visual Grounding）模型，大多像是一个" 只会看图说话 "的愣头青。它们盯着当前的这一帧画面，试图从 像素 里找答案。一旦指令模糊， 或者目标被遮挡，它们就很容易"指鹿为马"，甚至引发错误推理。 人类司机为什么不会弄错？因为我们会" 预判 "。 当我们听到指令时，大脑里会瞬间推演未来的画面：左边那辆车马上要转弯了，不符合"跟着"的语境；只有中间那辆车在加速直行，才是最可能的意图。 "在行动之前，先思考未来"。 受此启发，来自[澳门大学]的研究团队提出了全新的框架 ThinkDeeper。这是首个将世界模型（World Model）引入自动驾驶视觉定位的研究。这项工作不仅刷 ...

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 discusses the evolving recruitment landscape in the autonomous driving sector, highlighting a shift in demand from perception roles to end-to-end, VLA, and world model positions [2] - A new advanced course focused on end-to-end production in autonomous driving has been designed, emphasizing practical applications and real-world experience [2][4] Course Overview - The course is structured to cover various core algorithms, including one-stage and two-stage end-to-end methods, navigation information applications, reinforcement learning, and trajectory optimization [2] - The course aims to provide in-depth knowledge and practical skills necessary for production in autonomous driving, with a focus on real-world applications and challenges [2][4] Chapter Summaries - **Chapter 1: Overview of End-to-End Tasks** Discusses the integration of perception tasks and the learning-based design of control algorithms, which are essential skills for companies in the end-to-end era [7] - **Chapter 2: Two-Stage End-to-End Algorithm Framework** Introduces the modeling methods of two-stage frameworks and the information transfer between perception and planning, including practical examples [8] - **Chapter 3: One-Stage End-to-End Algorithm** Focuses on one-stage frameworks that allow for lossless information transfer, presenting various methods and practical learning experiences [9] - **Chapter 4: Production Application of Navigation Information** Covers the critical role of navigation information in autonomous driving, detailing mainstream navigation map formats and their integration into models [10] - **Chapter 5: Introduction to RL Algorithms in Autonomous Driving** Explains the necessity of reinforcement learning in conjunction with imitation learning to enhance the model's ability to generalize [11] - **Chapter 6: Trajectory Output Optimization** Engages participants in practical projects focusing on algorithms based on imitation learning and reinforcement learning [12] - **Chapter 7: Safety Net Solutions - Spatiotemporal Joint Planning** Discusses post-processing logic to ensure model accuracy and stability in trajectory outputs, introducing common smoothing algorithms [13] - **Chapter 8: Experience Sharing on End-to-End Production** Provides insights on practical experiences in production, addressing data, models, scenarios, and strategies for system capability enhancement [14] Target Audience - The course is aimed at advanced learners with a foundational understanding of autonomous driving algorithms, reinforcement learning, and programming skills [15][17]

从公开数据看，现阶段特斯拉追赶Waymo面临挑战。据特斯拉最新财报电话会议，该公司在奥斯汀的 车队行驶里程达25万英里，旧金山湾区超过100万英里。而Waymo今年7月已宣布累计完全自动驾驶里 程达1亿英里。 Waymo订单增长与其快速扩张密切相关。媒体爆料后不久，电动汽车分析师Sawyer Merritt在社交媒体 披露，Waymo的Robotaxi服务目前在全美部署了2500辆车，覆盖旧金山湾区、洛杉矶、凤凰城、奥斯汀 和亚特兰大五座城市。 媒体最近的爆料显示，谷歌"姊妹"、Alphabet旗下自动驾驶公司Waymo的Robotaxi业务单周出行付费订 单不到八个月翻了将近一倍。 据媒体报道，Waymo的投资方老虎环球基金（Tiger Global）最近宣布启动新的风投基金PIP 17，在致信 投资者介绍这只计划募资22亿美元的新基金时意外披露，Waymo的每周付费订单已突破45万次。这个 数据意味着，周订单较今年4月的25万次增长了80%。 这一增长速度使Waymo在与特斯拉的Robotaxi竞争中进一步扩大领先优势。老虎环球基金在信中写道： "Waymo是自动驾驶领域的明确领导者，其产品比人类驾驶员安 ...

Investment Rating - The report maintains an "Outperform" rating for the company [4][58]. Core Insights - The company achieved revenue of 171 million yuan in Q3 2025, representing a year-on-year growth of 144.2% and a quarter-on-quarter increase of 34.4% [1][6]. - The net profit for Q3 2025 was -307 million yuan, an improvement from -1.043 billion yuan in Q3 2024 and -406 million yuan in Q2 2025 [1][6]. - The company is a global leader in L4 autonomous driving products and solutions, with significant advancements in commercializing its L4 products [3][24]. Financial Performance - In Q3 2025, the company's gross margin was 32.9%, a year-on-year increase of 26.4 percentage points [2][14]. - The adjusted net profit margin for Q3 2025 was -161.2%, showing a significant improvement from previous quarters [2][14]. - The company’s expense ratio decreased to 254.9% in Q3 2025, down 1024.4 percentage points year-on-year [2][19]. Business Segments - Product revenue in Q3 2025 was 79.2 million yuan, a remarkable year-on-year growth of 428.0%, driven by increased sales of Robotaxi and Robobus [1][7]. - Service revenue for the same period was 91.8 million yuan, up 66.9% year-on-year, primarily due to growth in smart data services and autonomous driving-related operational support [1][7]. Market Expansion and Partnerships - The company has accelerated the commercialization of its L4 products, obtaining autonomous driving licenses in multiple countries, including Switzerland, China, UAE, Saudi Arabia, Singapore, France, Belgium, and the USA [3][25]. - In November 2025, the company launched L4 Robotaxi commercial operations in Abu Dhabi in partnership with Uber, marking a significant milestone in the Middle East [32]. - The company has also expanded its Robobus services to various cities, including operations in UAE and Belgium [43][44]. Future Projections - The report has adjusted revenue forecasts for 2025-2027 to 5.51 billion yuan, 10.00 billion yuan, and 18.66 billion yuan, respectively, reflecting a downward revision due to domestic L4 policy openings [58]. - The projected net losses for the same period are -14.16 billion yuan, -13.10 billion yuan, and -10.18 billion yuan [58].

Investment Rating - The report maintains an "Outperform" rating for the company [4][58]. Core Insights - The company achieved a revenue of 171 million yuan in Q3 2025, representing a year-on-year growth of 144.2% and a quarter-on-quarter increase of 34.4% [1][6]. - The net profit for Q3 2025 was -307 million yuan, an improvement from -1.043 billion yuan in Q3 2024 and -406 million yuan in Q2 2025 [1][6]. - The company is a global leader in L4 autonomous driving products and solutions, with significant advancements in the commercialization of its L4 products [3][24]. Financial Performance - In Q3 2025, the company's gross margin was 32.9%, a year-on-year increase of 26.4 percentage points [2][14]. - The adjusted net profit margin for Q3 2025 was -161.2%, showing a significant improvement from previous quarters [2][14]. - The company’s expense ratio decreased to 254.9% in Q3 2025, down 1024.4 percentage points year-on-year [2][19]. Business Segments - The product business revenue in Q3 2025 was 79.2 million yuan, a remarkable year-on-year growth of 428.0%, driven by increased sales of Robotaxi and Robobus [1][7]. - The service business revenue reached 91.8 million yuan in Q3 2025, up 66.9% year-on-year, primarily due to growth in smart data services and autonomous driving-related operational and technical support services [1][7]. Market Developments - The company has accelerated the commercialization of its L4 products, with Robotaxi receiving a pure unmanned license from the Swiss Federal Roads Office [3][25]. - As of October 31, 2025, the company deployed over 300 Robotaxi vehicles in Guangzhou, achieving significant operational milestones [3][28]. - The company has partnered with Uber to launch L4 Robotaxi commercial operations in Abu Dhabi, marking a significant expansion in the Middle East [3][32]. Future Projections - The revenue forecast for 2025-2027 has been adjusted to 5.51 billion yuan, 10.00 billion yuan, and 18.66 billion yuan, respectively, reflecting a growth rate of 68.7%, 113.5%, and 58.9% [58]. - The projected net profit for the same period is expected to be -14.16 billion yuan, -13.10 billion yuan, and -10.18 billion yuan [58].

责任编辑：张俊 SF065 伍德还加大了对自动驾驶领域的布局，通过ARK自主技术与机器人ETF（ARKQ）以约160万美元买入 17326股文远知行（WeRide，WRD）股票。此前摩根大通下调了该股目标价，导致其股价近期下跌 2.8%，但摩根大通仍维持"买入"评级，理由是随着无人驾驶出租车（robotaxi）监管政策逐步完善，该 股具备长期上涨潜力。 特斯拉则遭到减持，ARKK以92.3万美元出售了2100股，此举正值分析师对其合理估值表示担忧之际。 其他投资组合调整还包括GeneDX Holdings（WGS）、Tempus AI（TEM）和Arcturus Therapeutics （ARCT）。 著名投资者凯茜·伍德（Cathie Wood）旗下的ARK Invest基金周一调整了持仓，增持中国科技巨头百度 （BIDU）股份，同时削减了对特斯拉（TSLA）的持仓。 ARK创新ETF（ARKK）以约670万美元购入51263股百度股票。 花旗分析师Alicia Yap近期将百度的目标价上调至181美元，暗示其股价有近40%的上涨空间。她认为， 中国人工智能和云服务的持续增长正推动市场对该股的兴趣。 著名 ...

新华社阿布扎比12月9日电（记者温新年 赵丹亮）德国车企梅赛德斯-奔驰、中国科技企业北京初 速度科技有限公司、阿联酋出行运营商卢莫公司日前在阿联酋首都阿布扎比共同宣布，三方合作的豪华 自动驾驶出租车项目将于2026年在当地投入商业运营，并将逐步拓展至全球更多市场。 据悉，该自动驾驶出租车基于新一代奔驰S级轿车量产平台打造，将奔驰车的豪华舒适与初速度研 发的自动驾驶技术深度融合，再结合卢莫公司本地化运营体系，旨在为全球自动驾驶出行树立新标杆。 北京初速度科技有限公司首席执行官曹旭东指出，三方合作将奔驰的工程经验、初速度的人工智能 技术与卢莫公司的运营能力相融合，将为用户带来兼具安全与豪华的全新出行体验。 据介绍，奔驰与北京初速度科技有限公司自2017年起开展合作，双方共同开发的智能辅助驾驶系统 已搭载于今年11月上市的奔驰全新纯电CLA车型，未来将覆盖更多奔驰车型。 【纠错】 【责任编辑:焦鹏】 阿联酋总统战略研究与先进技术事务顾问、先进技术研究委员会秘书长费萨尔·阿卜杜勒阿齐兹·班 奈表示，此次合作体现了自动驾驶领域的国际水准，阿联酋拥有清晰的战略愿景和良好的创新环境，为 未来出行方式提供了理想的试验场。 梅 ...

（原标题：老虎环球新基金启动，聚焦这一领域！） 全球知名投资机构老虎环球基金（Tiger Global Management）宣布启动其最新风险投资基金"PIP 17"（Private Investment Partners 17），计划募资22亿美元。这一举措标志着该公司从2021年的"广撒 网"式投资策略转向更为审慎和聚焦的模式。 新基金将聚焦人工智能 此外，老虎环球基金还斩获了阿里巴巴、京东、新东方、小米等多个IPO，还因成功投资领英 （LinkedIn）、Facebook、网飞（Netflix）等多个互联网公司，精准把握住了互联网爆发增长的时期， 再到后来云服务和大数据应用兴起的时期，孵化出一个又一个行业巨头。 成立初期，老虎环球基金的投资风格相对保守。数据显示，2000年至2010年期间，老虎环球基金平均每 年投出30个项目；而在接下来的10年中，老虎环球基金的布局逐渐提速，这一数字增长到每年45次。 老虎环球基金在投资者电话会议中透露，PIP 16基金迄今回报率达33%，而PIP 15基金回报率为16%。 这一反弹主要得益于其在OpenAI和自动驾驶公司Waymo的早期布局。 公开资料显示，老虎环 ...