Core Insights - Mobileye Global Inc is acquiring humanoid robot startup Mentee in a cash and stock deal valued at approximately $900 million, marking its first foray into physical AI [1] - The stock price of Mobileye has shown a rebound from its record low of $10.04 on December 18, with a 17.4% increase since the beginning of 2026 [2] - The trading volume of call options has surged, with 44,000 calls exchanged, significantly higher than the average, indicating bullish sentiment among options traders [3] Stock Performance - Mobileye's stock was last trading near breakeven at $12.18, having previously surged to $14.33 following the acquisition news [1] - The stock has faced resistance at the 200-day moving average, which has limited further gains [2] Short Interest and Trading Activity - Short interest in Mobileye remains at 13.5% of the stock's available float, although it has been unwinding, indicating some short covering activity [4] - It would take over four days for short sellers to cover their positions based on the average trading pace of Mobileye [4]

Market Overview - U.S. stock futures were mixed following a higher close on Tuesday, with major benchmark indices showing varied performance [1] - Investors are preparing for significant economic releases, including the ADP employment report and ISM services index [1] - The Dow Jones increased approximately 1% to reach new record highs during a rotation into blue-chip stocks [1] Treasury Yields and Market Sentiment - The 10-year Treasury bond yielded 4.15%, while the two-year bond was at 3.46% [2] - Market expectations indicate an 83.9% likelihood that the Federal Reserve will keep interest rates unchanged in January [2] Stock Performance - AAR Corp. (NYSE:AIR) shares rose 5.07% after reporting better-than-expected second-quarter results and a strong sales forecast for the current quarter, projecting sales between $813.840 million and $827.404 million, exceeding market estimates of $793.438 million [6] - Penguin Solutions Inc. (NASDAQ:PENG) saw a 4.41% increase in premarket trading after reporting better-than-expected first-quarter financial results [6] - Mobileye Global Inc. (NASDAQ:MBLY) shares jumped 11% following a definitive agreement to acquire Mentee Robotics for approximately $900 million, aiming to integrate its autonomous driving technologies with Mentee's robotics platform [6] - Ventyx Biosciences Inc. (NASDAQ:VTYX) advanced 67.76% amid reports of advanced acquisition talks with Eli Lilly & Co. for over $1 billion [6] Sector Performance - On Tuesday, materials, health care, and industrials stocks showed the strongest gains on the S&P 500, while energy and communication services sectors closed lower [9] Analyst Insights - BlackRock maintains a positive outlook for 2026, advocating a "risk-on" stance driven by structural shifts rather than traditional macroeconomic indicators, expecting continued strength in U.S. equities supported by strong corporate earnings and AI themes [11][12] - The firm emphasizes the importance of adapting to changing economic conditions, noting that traditional anchors like stable inflation have weakened [13] Upcoming Economic Data - Key economic data to be released includes December's ADP employment report, ISM services index, job openings data, and U.S. factory orders [14] Commodities and Global Markets - Crude oil futures fell by 1.02% to around $56.55 per barrel, while gold spot prices decreased by 0.70% to approximately $4,463.46 per ounce [17] - Bitcoin traded 1.74% lower at $91,732.77 per coin [17] International Market Performance - Asian markets closed mixed, with South Korea's KOSPI and Australia's ASX 200 indices rising, while indices in China, Japan, Hong Kong, and India fell [18] - European markets were mostly higher in early trade [18]

人民财讯1月7日电，1月6日，百度旗下萝卜快跑正式获得迪拜道路与交通管理局(RTA)颁发的全无人驾 驶测试许可，成为迪拜首个且目前唯一获准开展全无人测试的平台。这为2026年一季度萝卜快跑启动全 无人商业化运营打开了关键的准入通道。根据规划，萝卜快跑在迪拜地区的全无人驾驶车队规模将扩充 至1000辆以上。同日，萝卜快跑在海外建设的首个无人驾驶一体化运营基地于迪拜正式启用。 ...

格隆汇1月7日｜自动驾驶技术公司Mobileye公布，将以现金加股票的方式收购以色列人形机器人初创公 司Mentee Robotics，交易价值9亿美元。公司称，交易包括6.12亿美元现金和最多2,622.97万股Mobileye A类普通股，预计于今年第一季完成交易。 ...

Group 1 - Baidu's AI chip subsidiary, Kunlun Chip Technology Co., Ltd., has submitted a listing application to the Hong Kong Stock Exchange, aiming for an IPO [1] - Following the announcement, Baidu's stock price surged by 9.35%, reflecting market excitement over the potential of Kunlun Chip as a significant player in the domestic chip market [2] - There is speculation about whether Baidu's autonomous driving service, "Luobo Kuaipao," will also pursue an independent listing, given its growing recognition and market valuation [4] Group 2 - The potential split of Baidu's businesses, including Kunlun Chip and Luobo Kuaipao, is seen as a strategy for value realization, allowing for better market valuation and risk management [5][12] - Baidu's core advertising business is estimated to have a conservative valuation of $114 billion, while its AI cloud business could be valued at approximately $259 billion [6][9] - The autonomous driving segment, represented by Luobo Kuaipao, is projected to generate around $2.8 billion in revenue by 2025, with a potential valuation of $70 billion based on market comparisons [6][7] Group 3 - Historical examples of successful business splits within Baidu, such as the financial services group "Duxiaoman," demonstrate the benefits of independent operations [13] - The challenges faced by Baidu's various segments, including the need to overcome "big company syndrome," highlight the potential advantages of splitting into independent entities [16][18] - The competitive landscape for autonomous driving services is intensifying, with the need for Luobo Kuaipao to establish its own identity and operational independence to attract talent and investment [20][24]

Core Insights - The article emphasizes the importance of deep learning in the fields of automation and computer science, particularly for students in these areas to explore cutting-edge topics such as VLA, end-to-end learning, and world models [2][3] - It highlights the need for newcomers to engage with research papers and discussions to develop their own ideas and methodologies [2] - The article introduces a paper guidance service aimed at assisting students with various aspects of research paper writing and publication [3][4][6] Group 1 - The article suggests that students from computer science and automation backgrounds should focus on deep learning, with specific recommendations for topics like VLA, end-to-end learning, and world models [2] - For mechanical and vehicle engineering students, it recommends starting with traditional PnC and 3DGS due to their lower computational requirements and ease of entry [2] - The article encourages new researchers to learn from failures and emphasizes the importance of developing personal insights through extensive reading and communication [2] Group 2 - The paper guidance service offers support in selecting research topics, full process guidance, and experimental assistance [6] - The service has a high acceptance rate for papers submitted to top conferences and journals, including CVPR, AAAI, and ICLR [7] - Pricing for the guidance service varies based on the level of the paper, and further details can be obtained by contacting the research assistant [8]

自动驾驶车端世界模型方向 招工程师/博后/实习生 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 清华大学智能绿色车辆与交通全国重点实验室招聘工程师/博后/实习生，感兴趣的可以联系柱哥投递简历或邮 箱自行投递简历。 【岗位目标】 面向端到端自动驾驶核心技术需求，从事车端世界模型的研究与工程化落地。构建融合物理先验、时序一致 性与行为预测能力的世界模型架构，实现复杂驾驶场景的理解、预测与生成，支撑自动驾驶系统的感知、预 测、规划一体化能力建设，推动端到端自动驾驶技术的工程化应用。 【核心职责及次要职责】 核心职责： 次要职责： 1. 研究与开发车端世界模型核心架构，融合物理先验、因果推理、时序一致性与行为预测能力； 2. 构建驾驶场景时空表征与预测模型，实现交通参与者行为预测、场景演化推理与长期规划； 3. 研发基于Transformer、Diffusion、Neural Fields等前沿架构的场景生成与仿真模型； 4. 设计多模态输入融合方案，实现图像、点云、地图、轨迹等多源信息的统一编码与推理； 5. 完成世界模型在车端平台的部署优化，满足实时性与资源 ...

Core Message - Seyond is showcasing its advanced LiDAR solutions at CES 2026, emphasizing the importance of deployment-ready technology for real-world autonomy across various applications [1][3]. Group 1: Seyond's CES Presence - Seyond's booth features an interactive two-level experience demonstrating how LiDAR technology supports real-world autonomy in automotive, robotics, and intelligent infrastructure [2]. - The company promotes a message that real-world autonomy requires proven, scalable perception, offering a complete sensing portfolio for customers [3]. Group 2: Key Showcases - A highlight of Seyond's booth is the Z5 RoboVan from Zelostech, showcasing high-performance perception for autonomous vehicle platforms [4]. - The Z5 RoboVan integrates Seyond's LiDAR technology, illustrating its capabilities in environment sensing and decision-making for logistics and commercial applications [5][6]. Group 3: Live Demonstrations - Seyond will demonstrate its latest LiDAR platforms, including Hummingbird and Robin E1X, showcasing their performance in real-world environments [7][8]. - Hummingbird offers compact, wide-field-of-view perception, while Robin E1X enhances range and point cloud quality for various applications [8]. Group 4: Company Overview - Seyond is a global provider of advanced LiDAR solutions, focusing on performance, reliability, and scalability to enable real-world autonomy [9].

Core Viewpoint - The article discusses the implementation of a unified Trigger framework for automatic detection, attribution, and management of anomalies in autonomous driving systems, transitioning from manual log analysis to automated problem identification and classification [2][5][69]. Group 1: Transition from Manual to Automated Processes - The traditional method of bug detection in autonomous driving relies heavily on experienced personnel and separate logic for cloud, vehicle, and simulation, making it difficult to systematically identify and prioritize issues [3][4]. - The goal is to enable anomalies to be automatically identified and structured into problem samples without human intervention, leading to a more efficient problem management system [5][6]. Group 2: Definition and Functionality of Trigger - The Trigger framework is defined as a combination of feature engineering and tokenization, where raw logs are transformed into structured tokens for classification [7][8]. - The framework aims to unify the logic across vehicle, cloud, and simulation environments, ensuring consistent definitions of events and problems [10][15]. Group 3: Trigger Framework Design - The Trigger framework is designed with three layers: Trigger definition, Trigger runtime, and Trigger management, allowing for a standardized execution interface across platforms [16][19]. - Each Trigger has a unique identifier and metadata, including dependencies and output labels, facilitating its integration into various systems [19][20]. Group 4: Case Development from Anomalies - Anomalies detected by the Trigger lead to the creation of structured cases, which are further analyzed using historical data to provide evidence and insights [40][41]. - The process involves breaking down a road case into multiple bad cases based on module or issue classification, allowing for targeted problem resolution [41][42]. Group 5: Classification and Automation - The classification of issues has evolved from rule-based systems to utilizing LLMs (Large Language Models) for more nuanced categorization based on token sequences generated by the Trigger [46][48]. - The automation of ticket generation and regression testing is integrated into the workflow, reducing manual effort and improving response times for identified issues [52][54]. Group 6: Continuous Improvement and Feedback Loop - The system incorporates a feedback loop where modifications by developers on classified cases provide supervision signals to improve the classification accuracy over time [67][70]. - The framework supports the identification of head problems through clustering and analysis of case similarities, enhancing the overall problem management process [68][72].

Core Viewpoint - The article emphasizes the establishment of a comprehensive community for autonomous driving, aiming to provide a platform for knowledge sharing, technical discussions, and career opportunities in the field [4][17]. Group 1: Community Development - The "Autonomous Driving Heart Knowledge Planet" has been created to address the high trial-and-error costs for newcomers in the autonomous driving industry, offering a structured learning environment [4][5]. - The community has grown to over 4,000 members and aims to expand to nearly 10,000 within two years, focusing on both academic and industrial needs [5][18]. - Various activities such as face-to-face meetings, expert interviews, and industry research will continue to be organized to meet the diverse needs of members [4][5][18]. Group 2: Learning Resources - The community has compiled over 40 technical learning paths, covering topics from entry-level to advanced autonomous driving technologies [7][18]. - Members have access to exclusive video tutorials and documents that facilitate learning in areas such as perception fusion, SLAM, and decision-making [11][18]. - A comprehensive list of open-source projects and datasets related to autonomous driving has been made available to assist members in their research and projects [35][37]. Group 3: Industry Insights - The community plans to conduct industry research focusing on the scaling of autonomous driving technologies, particularly in the L4 domain, which is expected to regain attention in the coming year [4][18]. - Regular discussions with industry experts will provide insights into the latest trends, challenges, and opportunities in the autonomous driving sector [7][18]. - The community aims to connect members with job opportunities in leading companies within the autonomous driving industry, facilitating career advancement [11][20].