张俊杰表示：此前围绕他婚姻和创业的诸多谣言，让其本人及家人受到了很大伤害，"有必要站出来说话"。 整理 ｜娃娃菜 点击上方【36氪随声听】，一键收听大公司热门新闻。听完音频记得添加进入 【我的小程序】 中哟！ 霸王茶姬创始人发朋友圈辟谣 日前霸王茶姬创始人张俊杰本人通过朋友圈证实了婚讯，并透露已于今年6月登记领证。在感谢公众祝福的同时，张俊杰也表示：此前围绕他婚姻和创业 的诸多谣言，让其本人及家人受到了很大伤害，"有必要站出来说话"。"我郑重声明：在我遇见我妻子（高海纯）前，从未有过婚姻，也从未发生'茶叶富 商残疾女儿'的任何故事。"（新浪财经） 俞敏洪再发全员信道歉，回应被叫老登 11月22日深夜，俞敏洪再发全员全员信再向员工道歉，并详细解释了自己写信的缘由和初衷。对于"用员工的血汗钱旅游"的说法，俞敏洪认为这样的表达 不妥，你在努力工作的同时，老板们也在拼命努力，并承担更大的风险，确保公司的正常发展。 此外，俞敏洪还表示尽管被叫"老登"，自己还是要说几句： 尽管有些人骂我老登，但有关工作和成长的事情，我还是要啰嗦几句。如果你在新东方工作，并且没有放弃这份工作，我觉得你就有责任维护新东方的发 展和形象。如果 ...

Core Insights - The article discusses Xiaomi's MiMo-Embodied, a cross-domain foundational model that integrates autonomous driving and embodied intelligence, achieving state-of-the-art (SOTA) performance across 29 benchmark tests [5][24]. Group 1: Model Overview - MiMo-Embodied is the first open-source unified model that combines tasks from autonomous driving and embodied intelligence into a single framework, enabling positive transfer and mutual enhancement between the two domains [7][8]. - The model supports three core capabilities in autonomous driving: environment perception, state prediction, and driving planning, as well as three core capabilities in embodied intelligence: usability prediction, task planning, and spatial understanding [8]. Group 2: Training and Data Strategy - The model employs a multi-stage training strategy with carefully designed datasets to overcome cross-domain task interference, leading to performance improvements [9][20]. - The training process consists of four stages: general and embodied knowledge learning, autonomous driving knowledge learning, chain-of-thought (CoT) reasoning fine-tuning, and reinforcement learning (RL) fine-tuning [21][27]. Group 3: Performance Metrics - MiMo-Embodied has achieved SOTA in usability prediction across five benchmarks, outperforming models like Qwen2.5-VL and GPT-4o [24]. - In task planning, it demonstrates strong long-range reasoning and causal inference capabilities, particularly in the RoboVQA benchmark [24]. - The model excels in spatial understanding and environment perception, leading in nine benchmarks, especially in 3D scene reasoning and spatial language localization [24][25]. Group 4: Comparative Analysis - The model's performance in various benchmarks shows significant improvements over previous models, with an average performance increase of 4% in embodied tasks and 8.1% in autonomous driving tasks compared to mixed training approaches [27][37]. - MiMo-Embodied's architecture and training strategy allow it to maintain high performance across both domains, achieving an average score of 62.4% in embodied tasks and 63.3% in autonomous driving tasks [37].

Core Viewpoint - The article emphasizes the importance of high-quality research papers for students, especially those pursuing master's and doctoral degrees, as a means to enhance their academic and career prospects. It highlights the challenges faced by students in producing quality research due to lack of guidance and the need for professional assistance in the research process [1][5]. Group 1: Challenges Faced by Students - Many students struggle to secure jobs due to average research outcomes and seek to pursue further studies to alleviate employment pressure, but their master's performance significantly influences their doctoral applications [1]. - Students often encounter difficulties in research paper writing, such as topic selection, framework confusion, and weak argumentation, especially when left without proper mentorship [1][9]. Group 2: Services Offered - The company provides professional guidance for research paper writing, including personalized support from experienced mentors, to help students navigate the research process effectively [4][12]. - The structured program includes defining research directions, literature review, experimental design, data collection, drafting, and submission, ensuring a comprehensive approach to research paper development [4][12]. Group 3: Expertise and Success Rate - The company boasts a team of over 300 dedicated instructors specializing in fields like autonomous driving and robotics, with a high acceptance rate of 96% for students they have guided in the past three years [5]. - The instructors are affiliated with top global universities and have published numerous papers in prestigious conferences and journals, ensuring high-quality mentorship [5]. Group 4: Target Audience - The services are tailored for students in computer science and related fields who face challenges such as lack of guidance, need for research experience, or aspirations for academic advancement [10][9]. - The program is suitable for those aiming to publish in high-impact journals or conferences, as well as for students looking to enhance their resumes for further studies or job applications [10][12].

Core Viewpoint - Xiaomi is making significant investments in intelligent driving technology, focusing on safety, comfort, and efficiency, with safety being the top priority in their development strategy [4][7]. Development Progress - Xiaomi's intelligent driving has progressed through several versions: from high-precision maps for highway NOA (version 24.3) to urban NOA (version 24.5), and moving towards light map and no map versions (version 24.10) [7]. - The company is advancing through three stages of intelligent driving: 1.0 (rule-driven), 2.0 (data-driven), and 3.0 (cognitive-driven), with a focus on VLA (Vision Language Architecture) for the next production phase [7][10]. World Model Features - The world model introduced by Xiaomi has three essential characteristics: diversity in generated scenarios, multimodal input and output, and interactive capabilities that influence vehicle behavior [8][9]. - The world model is designed to enhance model performance through cloud-based data generation, closed-loop simulation, and reinforcement learning, rather than direct action outputs from the vehicle [10]. VLA and Learning Models - VLA is described as an enhancement over end-to-end learning, integrating high-level human knowledge (traffic rules, values) into the driving model [13]. - Xiaomi's development roadmap includes various model training stages, from LLM pre-training to embodied pre-training, with recent advancements in MiMo and MiMo-vl models [13]. Community and Knowledge Sharing - The "Automated Driving Heart Knowledge Sphere" community aims to provide a comprehensive platform for learning and sharing knowledge in the field of autonomous driving, with over 4,000 members and plans to expand [15][26]. - The community offers resources such as technical routes, video tutorials, and Q&A sessions to assist both beginners and advanced learners in the autonomous driving sector [27][30].

Core Insights - The article emphasizes the growing demand for end-to-end production talent in the automotive industry, highlighting a paradox where job seekers are abundant, yet companies struggle to find qualified candidates [1][3]. Course Overview - A newly designed end-to-end production course aims to address the skills gap in the industry, focusing on practical applications and real-world scenarios over three months [3][5]. - The course covers essential algorithms such as one-stage and two-stage end-to-end frameworks, reinforcement learning applications, and trajectory optimization techniques [5][10]. Course Content - **Chapter 1: Overview of End-to-End Tasks** - Discusses the integration of perception tasks and the learning-based control algorithms that are becoming mainstream in autonomous driving [10]. - **Chapter 2: Two-Stage End-to-End Algorithms** - Introduces the two-stage framework, its modeling methods, and the flow of information between perception and planning [11]. - **Chapter 3: One-Stage End-to-End Algorithms** - Focuses on one-stage frameworks that allow for lossless information transfer, enhancing performance compared to two-stage methods [12]. - **Chapter 4: Application of Navigation Information** - Explains the critical role of navigation data in autonomous driving and how it can be effectively integrated into end-to-end models [13]. - **Chapter 5: Introduction to Reinforcement Learning Algorithms** - Highlights the necessity of reinforcement learning to complement imitation learning, enabling machines to generalize better [14]. - **Chapter 6: Trajectory Output Optimization** - Covers practical projects involving imitation learning and reinforcement learning algorithms for trajectory planning [15]. - **Chapter 7: Contingency Planning - Spatiotemporal Joint Planning** - Discusses post-processing logic to ensure reliable trajectory outputs, including smoothing algorithms [16]. - **Chapter 8: Experience Sharing in End-to-End Production** - Provides insights on practical strategies and tools for enhancing system capabilities in real-world applications [17]. Target Audience - The course is designed for advanced learners with a foundational understanding of autonomous driving algorithms, reinforcement learning, and programming skills [18][19]. Course Schedule - The course is set to begin on November 30, with a structured timeline for unlocking chapters and providing support through offline videos and online Q&A sessions [20].

Core Insights - The article discusses the transition from autonomous driving to embodied intelligence, highlighting the differences in challenges and solutions between the two fields [2] - It emphasizes the importance of documenting past experiences in autonomous driving, even if they did not receive widespread attention, as they may provide practical insights for others in the field [2] Research Areas Summary - **Sparse4D Series**: A multi-sensor fusion perception framework that challenges the conventional BEV (Bird's Eye View) approach, arguing that it does not significantly enhance information while incurring high computational costs. The Sparse4D series aims to achieve efficient perception through sparse queries and projections [6][7] - **SparseDrive**: An attempt to extend the capabilities of the Sparse4D model into end-to-end planning, integrating online mapping and motion planning tasks. It successfully executed five tasks, including detection and tracking, but faced challenges in closed-loop performance evaluation [13][15] - **EDA & UniMM**: EDA introduces a dynamic anchor strategy for trajectory prediction, improving model convergence and accuracy. UniMM unifies existing traffic flow simulation models, addressing key performance factors in agent simulation [16][20] - **DriveCamSim**: A sensor simulation system designed to evaluate autonomous driving models efficiently. It focuses on generating sensor signals with high fidelity and controllability, addressing the limitations of traditional physical engine-based simulations [22][24] - **LATR**: A foundational model for intelligent driving that leverages large datasets for unsupervised training, aiming to understand the semantics of driving scenarios. It integrates multiple tasks into a unified framework, demonstrating effective performance across various driving tasks [26][27] Conclusion and Future Outlook - The seven modules discussed form the core link of the autonomous driving system, indicating a correct technological path. The industry is moving towards maturity in end-to-end models, with significant performance improvements for companies adopting these approaches. Future developments should focus on efficient evaluation systems and the potential of reinforcement learning to enhance model performance [30][31]

Core Insights - The article discusses the advancements in autonomous driving technology, particularly focusing on the development and implementation of VLA (Vehicle-Like Action) systems, highlighting the transition from perception-based approaches to VLA-based methodologies [14]. Group 1: VLA Development - The article reflects on the evolution of VLA technology over the past year, noting a shift from academic research to practical applications in the industry, culminating in the announcement of Xiaopeng's VLA 2.0 [14]. - It emphasizes the importance of VLA as a means to enhance the driving experience by mimicking human-like decision-making processes, akin to a "sixth sense" in driving [14]. Group 2: Research and Collaboration - The article mentions collaborative research efforts, such as the paper from The Chinese University of Hong Kong (Shenzhen) and Didi, which proposes a method for efficient reconstruction of dynamic driving scenes [14]. - It highlights the significance of ongoing discussions and knowledge sharing within the autonomous driving community, as seen in the roundtable discussions featuring industry experts [14].

Core Points - The procurement project for public transport vehicles in Zhuji has been successfully completed, with the announcement of the bidding results [1] - The procurement was organized by Zhuji Yueda Public Transport Co., Ltd. and was conducted through a single-source procurement method [1] - The bid was confirmed on November 20, 2025, and the evaluation committee has been established [3] Procurement Details - The procurement project name is "2025 Yueda Public Transport Vehicle Procurement Project" [1] - The procurement organization type is decentralized procurement entrusted agency [1] - The procurement method used was single-source [1] Evaluation and Contact Information - The evaluation committee members include Zhou Lijiang, Fu Wulin, Shou Zuping, Peng Zhanglin, Jiang Linyu, Li Qingguang, and representatives from the purchaser [3] - Contact information for the purchaser and the procurement agency is provided, including names and phone numbers [3] Announcement Period - The announcement is valid for three working days from the date of publication [3] Additional Information - Suppliers who believe their rights have been harmed by the bidding results can raise objections in writing within three working days from the announcement [4]

汽车行业周报 ❑ 个股行情回顾 汽车板块个股：本周，汽车板块个股下跌居多。其中，涨幅居前的个股有天 普股份（+14.7%）、路畅科技（+8.1%）和浙江荣泰（+7.6%）；跌幅居前 的个股有富临精工（-20.3%）、新朋股份（-18.1%）和立中集团（-17.2%）。 重点覆盖个股：本周，已覆盖个股周度下跌居多。其中，涨幅居前的个股为 康隆达（+8.3%）、华纬科技（+4.3%）和江淮汽车（+0.8%）；跌幅居前的 个股有道氏技术（-15.5%）、星源卓镁（-14.7%）和神驰机电（-11.7%）。 华为连发"两境"新品牌，特斯拉开放 Robotaxi 服务 中游制造/汽车 ❑ 风险提示：生产不及预期；消费不及预期；盈利不及预期。 11 月 16 日至 11 月 23 日，汽车行业整体下跌 5.1%。11 月 20 日，东风与华为 乾崑深度合作的 DH 项目中文品牌定名"奕境"，首款车型将于明年 4 月的北 京车展发布，后续每年至少有一款全新车型上市。同日，启境品牌将在华为乾 崑生态大会上正式发布，明年启境计划推出两款车型，首款车型将于明年 6 月 上市交付。 证券研究报告|行业定期报告 2025 年 11 ...

Core Insights - The strategic investment of over 3.6 billion yuan from China FAW Group into Zhuoyue Technology marks a significant development in the intelligent driving sector, with Zhuoyue's post-investment valuation exceeding 10 billion yuan [1][3] - Zhuoyue Technology will continue to operate independently, maintaining its existing management team and technological direction, while benefiting from China FAW's resources and business collaboration [1][3] Company Overview - Zhuoyue Technology, originally the automotive division of DJI, was established in 2016 and became an independent entity in 2023, rebranding itself in June 2024 [3] - The company focuses on developing high-level intelligent driving systems that cover various scenarios, including highways, urban areas, and parking, with its core product being the Chengxing platform [3][4] Market Position and Strategy - Zhuoyue Technology aims to penetrate the fuel vehicle market, which is seen as a strategic breakthrough point amidst increasing competition in the intelligent driving sector [4] - The company has established partnerships with several major automotive manufacturers, including China FAW, Volkswagen, and BYD, to expand its ecosystem [4][5] Investment Landscape - The investment from China FAW is part of a broader trend where multiple automotive companies invest in Zhuoyue Technology while allowing it to remain independent, reflecting a shift in the intelligent driving industry's dynamics [6] - The automatic driving sector has seen significant financing activity in 2025, with approximately 20 financing events totaling around 35 billion yuan, indicating a growing interest in the commercialization of technology [6]