Group 1 - Tesla has officially obtained a ride-hailing permit for its Robotaxi service in Texas, allowing it to operate without a safety driver in the vehicle, although a staff member will monitor remotely [2] - The fare for Tesla's autonomous taxi service is set at approximately $4.2 per trip, equivalent to about 30.2 RMB [2] - Elon Musk believes that by the end of 2025, Tesla could provide autonomous ride-hailing services to half of the U.S. population [2] Group 2 - Following the passage of the "Big and Beautiful" Act, which ends electric vehicle tax credits on September 30, American consumers are rushing to purchase electric vehicles to benefit from a $7,500 tax credit [2] - In July, electric vehicle sales accounted for 9.1% of total passenger car sales, marking a historical high, with nearly 36,700 used electric vehicles sold in the same month [2] - It is anticipated that the third quarter may set a record for electric vehicle sales, while a significant drop in sales is expected in the fourth quarter [2] Group 3 - Ledo Automotive announced that its L90 model will not have a buyout option for a 60 kWh battery, but there is a demand for leasing such a battery, which the company is considering [2] - The L90 model is standardly equipped with an 85 kWh battery, aligning with the company's philosophy of "chargeable, swappable, and upgradeable" energy solutions [2] Group 4 - On August 9, the remote ethanol-hydrogen electric tractor and intercity passenger bus were officially launched in Hohhot, with 270 orders signed on the same day [2] - This event marks a significant step in promoting the ethanol-hydrogen ecosystem strategy in Hohhot [2]

Core Viewpoint - The furniture and autonomous driving industries are experiencing significant growth in production, financing, and recruitment, with a strong emphasis on practical technology and skilled talent acquisition [1][2]. Group 1: Industry Trends - The autonomous driving sector is seeing a surge in companies scaling up production and hiring, indicating a competitive job market where securing positions is challenging due to high skill requirements [1]. - The emergence of high-level autonomous driving demonstration zones, such as in Beijing, is fostering innovation in policy, technology, and commercialization [1]. Group 2: Learning and Community Resources - Several influential communities focused on embodied intelligence, autonomous driving, computer vision, and AI are recommended for systematic learning and skill enhancement [1]. - The "Automatic Driving Heart" community is the largest developer community in China, focusing on various technical aspects of autonomous driving, attracting significant attention from industry professionals [2]. - The "Computer Vision Research Institute" shares the latest research and practical applications in AI, emphasizing technology research and implementation [5]. - The "Embodied Intelligence Heart" community is the first full-stack technical exchange platform in China, covering a wide range of topics related to embodied intelligence [8].

目前自动驾驶和具身智能两个方向我们已经和业内主流的公司及相关高校建立起深度的合作，大模型方向 也正在快速搭建。我们不止聚焦在技术本身，更愿意和大家一起共创整个AI领域，分享认知成长的喜悦。 对于热门事件，我同样希望我们提供全网独一份的内容价值。 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 大家好，我们是自动驾驶之心/具身智能/大模型之心Tech团队。非常高兴在这里和你相遇，如果你也认同技 术内容可以改变世界，那你可能就是我们在找的人！ 我们在做什么？ 我们希望通过技术内容连接学术界和工业界，成为企业和学校沟通的桥梁，更乃至数十万的AI开发者和创 业者。我们致力于为大家带来全网最新最权威的技术信息，团队聚焦在自动驾驶、具身智能、大模型等AI 最前沿的技术领域，涵盖学术论文解读、业内量产方案分析、大模型评测、商业动态、行业招聘、开源项 目等，并通过公众号、社群、视频号、知乎、小红书、B站等平台进行内容分享、粉丝交流及企业联系。 工作时间： 不积跬步无以至千里，我们深知一个人的力量是有限的，所以我们期待更多优秀的小伙伴与我们一起同行~ 内容运营 - 实习生 ...

Core Viewpoint - The article emphasizes the ongoing evolution and critical phase of the autonomous driving industry, highlighting the transition from modular approaches to end-to-end/VLA methods, and the community's commitment to fostering knowledge and collaboration in this field [2][4]. Group 1: Industry Development - Since Google's initiation of autonomous driving technology research in 2009, the industry has progressed significantly, now entering a crucial phase of development [2]. - The community aims to integrate intelligent driving into daily transportation, reflecting a growing expectation for advancements in autonomous driving capabilities [2]. Group 2: Community Initiatives - The community has established a knowledge-sharing platform, offering resources across various domains such as industry insights, academic research, and job opportunities [2][4]. - Plans to enhance community engagement include monthly online discussions and roundtable interviews with industry and academic leaders [2]. Group 3: Educational Resources - The community has compiled over 40 technical routes to assist individuals at different levels, from beginners to those seeking advanced knowledge in autonomous driving [4][16]. - A comprehensive entry-level technical stack and roadmap have been developed for newcomers to the field [9]. Group 4: Job Opportunities and Networking - The community has established internal referral mechanisms with multiple autonomous driving companies, facilitating job placements for members [7][14]. - Continuous job sharing and networking opportunities are provided to create a complete ecosystem for autonomous driving professionals [14][80]. Group 5: Research and Technical Focus - The community has gathered extensive resources on various research areas, including 3D target detection, BEV perception, and multi-sensor fusion, to support practical applications in autonomous driving [16][30][32]. - Detailed summaries of cutting-edge topics such as end-to-end driving, world models, and visual language models (VLM) have been compiled to keep members informed about the latest advancements [34][40][42].

Group 1 - The company aims to connect academia and industry through technical content, focusing on cutting-edge AI fields such as autonomous driving, embodied intelligence, and large models [1] - The team has established deep collaborations with mainstream companies and relevant universities in the fields of autonomous driving and embodied intelligence, while rapidly building partnerships in the large model sector [1] - The company provides a variety of content including academic paper interpretations, industry production solutions, large model evaluations, business dynamics, industry recruitment, and open-source projects [1] Group 2 - The company is looking for interns to assist in academic paper selection, interpretation, and summarization in the fields of large models, autonomous driving, and embodied intelligence [3] - Interns are expected to have a strong passion for research and sharing knowledge related to technological advancements and events [3] - The internship offers a combination of salary, one-on-one mentorship, industry resource recommendations, and internal job referrals [5]

【导读】美俄计划达成停火协议 中国基金报记者 泰勒 大家周末好！今晚简单关注一下海外市场的表现！ 美股继续上涨 8月8日晚间，美 股三大指数上涨，道指涨超100点，纳指大涨0.8%，标普500指数涨超0.6%。 消息面上，有报道称，美 国跟俄罗斯正寻求达成一项停止乌克兰战争的协议，这一消息推动股市进一步走高，而油价一度小幅度 跳水。 知情人士表示，美俄官员正就领土问题进行磋商，准备促成特朗普与普京最快在下周举行一次 峰会。他们还表示，美国正在争取乌克兰及其欧洲盟友对该协议的支持，但这并不确定。 据知情人士透露，作为协议的一部分，俄罗斯将在乌克兰的赫尔松和扎波罗热地区沿当前战线停止进 攻。但他们也警告称，该协议的条款仍在不断变动中，最终可能有所不同。 目前尚不清楚莫斯科是否愿意放弃其目前占领的任何土地，包括欧洲最大的扎波罗热核电站。 据知情人士表示，该协议的核心是将战争"冻结"，为实现停火和后续的正式和平谈判铺平道路。此前美 国一直在推动俄罗斯先无条件停火，为和平谈判创造空间。 （原标题：今夜，大涨！美国、俄罗斯，重大突发！） 特朗普周五警告称，美国法院若阻止其关税政策，可能会引发严重的经济衰退。他强调该政策对 ...

Core Viewpoint - Tesla is shifting its AI strategy from an emphasis on internal full-stack development to a high level of collaboration with computing power suppliers, marked by the dissolution of its internal Dojo supercomputer team [1][2]. Group 1: Strategic Shift - The Dojo team, responsible for building Tesla's high-performance computing platform for training autonomous driving systems and AI models, has been disbanded [1]. - CEO Elon Musk stated that it is unreasonable for Tesla to allocate resources to develop two distinct AI chips and emphasized focusing efforts on the AI5 and AI6 chips [1][2]. - The decision to dissolve the Dojo team reflects a strategic adjustment, as Tesla has increasingly relied on external partners for chip procurement and computing resources, including Nvidia, AMD, and Samsung [2]. Group 2: Talent and Cost Considerations - Key personnel, including Dojo team leader Peter Bannon, are leaving the company, with around 20 core members joining a new AI startup, DensityAI [1][2]. - The high costs and long timelines associated with building and maintaining an internal supercomputing platform have led to a reduction in self-developed hardware, allowing Tesla to free up funds and manpower for commercialization efforts [2]. Group 3: Technical Implications - Analysts suggest that this decision may weaken Tesla's autonomous development capabilities in certain AI areas, as the Dojo project was expected to significantly enhance performance in processing autonomous driving video data and optimizing neural network models [3]. - The adjustment is likely to strengthen the positions of Nvidia, AMD, and Samsung in Tesla's AI infrastructure, potentially increasing their market share in the autonomous driving and AI training chip sectors [3]. - Tesla recently signed a $16.5 billion agreement with Samsung for the production of AI6 chips, indicating a continued reliance on external suppliers [3].

Core Viewpoint - The article discusses the significance of Vision-Language Models (VLM) in the context of autonomous driving, highlighting their ability to integrate visual perception and natural language processing to enhance vehicle understanding and interaction with complex road environments [4][19]. Summary by Sections What is VLM? - VLM stands for Vision-Language Model, which combines the capabilities of understanding images and text within a single AI system. It enables deep comprehension of visual content and natural language interaction, enhancing applications like image retrieval, writing assistance, and robotic navigation [6]. How to Make VLM Work Efficiently? - VLM processes raw road images into feature representations using visual encoders, such as Convolutional Neural Networks (CNN) and Vision Transformers (ViT). Language encoders and decoders handle natural language input and output, learning semantic relationships between tokens [8]. Key Mechanism of VLM - The alignment of visual features and language modules is crucial for VLM. Cross-attention mechanisms allow the language decoder to focus on relevant image areas when generating text, ensuring high consistency between generated language and actual scenes [9]. Training Process of VLM - The training process for VLM typically involves pre-training on large datasets followed by fine-tuning with specific datasets related to autonomous driving scenarios, ensuring the model can accurately recognize and respond to traffic signs and conditions [11]. Applications of VLM - VLM supports various intelligent functions, including real-time scene alerts, interactive semantic Q&A, and recognition of road signs and text. It can generate natural language prompts based on visual inputs, enhancing driver awareness and decision-making [12]. Real-time Operation of VLM - VLM operates in a "cloud-edge collaboration" architecture, where large-scale pre-training occurs in the cloud, and optimized lightweight models are deployed in vehicles for real-time processing. This setup allows for quick responses to safety alerts and complex analyses [14]. Data Annotation and Quality Assurance - Data annotation is critical for VLM deployment, requiring detailed labeling of images under various conditions. This process ensures high-quality training data, which is essential for the model's performance in real-world scenarios [14]. Safety and Robustness - Safety and robustness are paramount in autonomous driving. VLM must quickly assess uncertainties and implement fallback measures when recognition errors occur, ensuring reliable operation under adverse conditions [15]. Differences Between VLA and VLM - VLA (Vision-Language-Action) extends VLM by integrating action decision-making capabilities. While VLM focuses on understanding and expressing visual information, VLA encompasses perception, cognition, and execution, making it essential for real-world applications like autonomous driving [18]. Future Developments - The continuous evolution of large language models (LLM) and large vision models (LVM) will enhance VLM's capabilities in multi-modal integration, knowledge updates, and human-machine collaboration, leading to safer and more comfortable autonomous driving experiences [16][19].

Core Viewpoint - The furniture and autonomous driving industries are experiencing significant growth in production, financing, and recruitment, leading to a highly competitive job market where skilled professionals are in high demand [1]. Group 1: Industry Trends - The industry is focusing on practical technologies, with companies competing to secure talent with relevant skills [1]. - The job market is described as "highly competitive," making it difficult for candidates to secure positions despite the availability of openings [1]. Group 2: Recommended Learning Communities - "Smart Driving Frontier" is a comprehensive media platform dedicated to the autonomous driving sector, providing technical insights and industry news [1]. - "Computer Vision Research Institute" focuses on AI research and practical applications, sharing the latest algorithms and project experiences [3]. - "Visual Language Navigation" aims to create a professional platform for navigation technologies, sharing technical insights and industry news [5]. - "Embodied Intelligence Research Lab" emphasizes core areas such as reinforcement learning and multi-agent collaboration, providing research updates and practical case studies [6]. - "Embodied Intelligence Heart" is the largest community for embodied intelligence, covering various technical directions and encouraging collaboration among developers [7]. - "arXiv Daily Academic Express" offers daily updates on academic papers across multiple fields, including AI and robotics, facilitating quick access to relevant research [8]. - "Autonomous Driving Heart" is a community for developers in the autonomous driving field, focusing on various technical aspects and job opportunities [10].

Group 1 - The core viewpoint of the article highlights Hesai's stock performance and financial results, indicating a positive growth trajectory in revenue and net profit [1][2][3] - As of August 8, Hesai's stock price increased by 2.06%, reaching $22.75 per share, with a total market capitalization of $3.014 billion [1] - Financial data shows that Hesai's total revenue for the period ending March 31, 2025, is projected to be 525 million RMB, representing a year-on-year growth of 46.27%, while the net profit attributable to the parent company is expected to be -17.548 million RMB, reflecting a year-on-year increase of 83.59% [1] Group 2 - Hesai Group is a Cayman Islands-registered holding company primarily operating through its subsidiary, Shanghai Hesai Technology Co., Ltd., which was founded in 2014 [2] - The company specializes in advanced driver-assistance systems (ADAS) and LiDAR technology, positioning itself as a leader in the autonomous driving sector [2] - Hesai has a strong research and development capability with hundreds of patents globally, focusing on optical, mechanical, and electronic fields related to LiDAR technology [2] - The company's vision is to empower robots with high-performance, reliable, and cost-effective 3D sensors, enhancing human efficiency and comfort [2]