Core Viewpoint - The article emphasizes the importance of creating an engaging learning environment in the field of autonomous driving and AI, aiming to bridge the gap between industry and academia while providing valuable resources for students and professionals [1]. Group 1: Community and Resources - The community has established a closed loop across various fields including industry, academia, job seeking, and Q&A exchanges, focusing on what type of community is needed [1][2]. - The platform offers cutting-edge academic content, industry roundtables, open-source code solutions, and timely job information, streamlining the search for resources [2][3]. - A comprehensive technical roadmap with over 40 technical routes has been organized, catering to various interests from consulting applications to the latest VLA benchmarks [2][14]. Group 2: Educational Content - The community provides a series of original live courses and video tutorials covering topics such as automatic labeling, data processing, and simulation engineering [4][10]. - Various learning paths are available for beginners, as well as advanced resources for those already engaged in research, ensuring a supportive environment for all levels [8][10]. - The community has compiled a wealth of open-source projects and datasets related to autonomous driving, facilitating quick access to essential materials [25][27]. Group 3: Job Opportunities and Networking - The platform has established a job referral mechanism with multiple autonomous driving companies, allowing members to submit their resumes directly to desired employers [4][11]. - Continuous job sharing and position updates are provided, contributing to a complete ecosystem for autonomous driving professionals [11][14]. - Members can freely ask questions regarding career choices and research directions, receiving guidance from industry experts [75]. Group 4: Technical Focus Areas - The community covers a wide range of technical focus areas including perception, simulation, planning, and control, with detailed learning routes for each [15][29]. - Specific topics such as 3D target detection, BEV perception, and online high-precision mapping are thoroughly organized, reflecting current industry trends and research hotspots [42][48]. - The platform also addresses emerging technologies like visual language models (VLM) and diffusion models, providing insights into their applications in autonomous driving [35][40].

据济南公交集团相关负责人介绍，L4级自动驾驶车辆在线路运行过程中，所有驾驶操作都不需要人员 介入，车辆在运行场景下具备交通信号及道路标识标线识别、自主超车、自动避障、精确进站等功能。 除了车迷，许多家长也带着孩子前来体验，让孩子们在实践中感受科技的进步。 目前，L4级智能网联公交线路仅在周末时间运行，市民可随时关注"369出行"APP内的线路信息和预约 通道，合理安排时间进行试乘体验。 济南公交的L4级自动驾驶公交车"智小蓝"一亮相便引发广泛关注。一个月以来，穿梭在起步区道路上 的"智小蓝"公交，成为城市里一道移动的"科技风景线"。 公交车迷是首批"尝鲜者"中的主力军。不少济南本地车迷在乘车体验结束后兴奋地与车身合影打卡，在 社交平台分享这份独特的出行体验。更有外地车迷听闻消息后专程赶来，只为亲身感受L4级自动驾驶 的魅力。 齐鲁晚报.齐鲁壹点于泊升 今年6月28日，济南市首条L4级智能网联公交线路——智能网联公交2号线，正式面向市民乘客预约开 通。一个月来，共有近500名乘客通过预约亲身乘车体验了自动驾驶的新奇与便捷，真切地感受到智慧 交通的脉动。 ...

Group 1 - Goldman Sachs expresses a positive outlook on the expansion of commercial autonomous taxi services in Shanghai, where licensed operators can provide fully driverless taxi services to the public in designated areas and charge fees [1] - The development of the autonomous taxi industry will be supported by continuous technological advancements, decreasing material costs due to large-scale deployment, and the expansion of the ecosystem among operators, original equipment manufacturers (OEMs), and asset owners [1] - During the World Artificial Intelligence Conference (WAIC) held in July 2025, Shanghai issued demonstration operation licenses for intelligent connected vehicles to companies such as Pony.ai, Baidu Group, WeRide, Jinjiang Taxi, Dazhong Transportation, and SAIC Motor [1]

Financial Data and Key Metrics Changes - Grab reported a year-on-year growth of 21% in on-demand GMV in U.S. Dollars, or 18% on a constant currency basis [5][6] - Adjusted EBITDA growth was sustained for the fourteenth consecutive quarter, with trailing twelve months adjusted free cash flow expanding to $229 million [6] - The company achieved an all-time high in monthly transacting users (MTUs) [6] Business Line Data and Key Metrics Changes - Mobility transactions grew by 23% year-on-year, with GMV increasing by 19% year-on-year [14][15] - Delivery GMV accelerated to 19% year-on-year on a constant currency basis, driven by product-led initiatives [21] - Financial Services business saw total loan disposals reaching close to $3 billion on an annualized run rate basis [6] Market Data and Key Metrics Changes - The company is focusing on affordability to attract more price-sensitive users, which has been critical in the current macroeconomic environment [10][11] - In Indonesia, Grab participated in a government initiative to deliver nutritious meals, enhancing brand loyalty and user engagement [12] - In Thailand, Grab is collaborating with the government to support the tourism sector [13] Company Strategy and Development Direction - Grab aims to maintain growth momentum and accelerate on-demand GMV growth rates relative to 2024 levels while maintaining cost discipline [6][11] - The company is investing in product-led innovations to enhance user engagement and retention [10][11] - Grab is leaning into the autonomous vehicle (AV) opportunity, planning pilots and partnerships to support the transition [28][30] Management's Comments on Operating Environment and Future Outlook - Management expressed confidence in navigating macroeconomic uncertainties through product-led investments and partnerships with governments [10][11] - The outlook for on-demand GMV growth in 2025 is expected to accelerate compared to 2024 levels, with adjusted EBITDA in the second half anticipated to be stronger than the first half [13] - Management emphasized the importance of balancing growth and profitability, particularly in the delivery segment [79][85] Other Important Information - The advertising revenue run rate reached $236 million, growing at 45%, with expectations for continued growth due to increased penetration among merchants [67][70] - The company completed a $500 million buyback and has no immediate plans for new buyback programs [41] Q&A Session Summary Question: Outlook for Grab in the macro environment - Management is focused on affordability and has launched products to enhance user engagement, positioning the company well despite macro uncertainties [10][11] Question: Strategies driving increase in mobility transaction frequency - The growth in mobility transactions is attributed to reinvestment in scale economies and product-led growth strategies [14][15] Question: Delivery segment margin outlook - Despite the growth of affordable products, delivery segment margins have expanded, and management expects continued improvement [25][49] Question: Capital allocation post convertible bond raise - The company maintains a prudent approach to capital allocation, prioritizing organic growth while remaining open to M&A opportunities [39][41] Question: Competition in food delivery in Vietnam - Management noted that the AOV drop in mobility is a strategic decision to drive volume rather than a response to competitive pressures [35][36] Question: Long-term outlook for GrabMart - GrabMart is expected to grow faster than traditional food delivery, with significant potential in the online grocery market [60][62]

Group 1 - Li Xiang, CEO of Li Auto, reflects on being ousted by a trusted partner during a financial crisis at Autohome in 2008, ultimately choosing reconciliation over resentment [4][5][6] - Li Xiang emphasizes the importance of understanding others' perspectives to avoid self-harm and achieve personal peace [5][6] Group 2 - Former Vice President of Product at Haomo.ai, Cai Na, has joined Momenta, bringing valuable experience from her previous roles [9][10] - Momenta aims to achieve profitability by 2026 and is involved in various autonomous driving projects [10] Group 3 - Li Auto's new electric SUV, the Li i8, has undergone rigorous safety testing, showcasing its structural integrity during crash tests [11] - Controversy arose when the truck used in the testing was identified as a Chenglong truck, leading to safety concerns and legal responses from the truck manufacturer [11] Group 4 - Alibaba has adjusted its stock vesting schedule for new employees to 15%, 25%, 30%, and 30% over four years, effective from April 1, 2024 [14][15] - This change aims to provide more immediate stock benefits to employees compared to the previous vesting schedule [14][15] Group 5 - ByteDance's AI programming tool, Trae, has been accused of unauthorized data collection, prompting concerns over user privacy [16][17] - ByteDance responded by stating that data collection practices are in line with industry standards and do not involve personal identification [17] Group 6 - Xiaomi's new end-to-end driving assistance system for its SU7 series has been launched, with a reminder that it is not equivalent to full autonomous driving [13] - The system has seen significant improvements in highway scenarios, but users are urged to remain attentive while driving [13] Group 7 - Stellantis reported a net loss of €2.3 billion in the first half of the year, with a 13% decline in net revenue compared to the previous year [35][36] - The company faces challenges in various markets, including China and India, and plans to launch several new models by the end of the year [36][37] Group 8 - Samsung is reconsidering a $7 billion investment in advanced packaging production in Texas, driven by demand for advanced process chips [44] - This move aligns with the growing need for localized chip production in the U.S. market, particularly for companies like Tesla [44]

Core Insights - The article discusses the introduction of InvRGB+L, a novel inverse rendering model that integrates LiDAR intensity for reconstructing large-scale, relightable dynamic scenes from RGB+LiDAR sequences [4][26]. Group 1: Introduction of InvRGB+L - InvRGB+L is the first model to apply LiDAR intensity in inverse rendering, enhancing material estimation under varying lighting conditions [4]. - Traditional methods primarily rely on RGB inputs, often leading to inaccurate material estimates due to visible light interference [4]. Group 2: Key Innovations - The model introduces two key innovations: a physics-based LiDAR shading model and RGB-LiDAR material consistency loss, which improve the rendering results of complex scenes [4][7]. - The physics-based LiDAR shading model accurately models the relationship between LiDAR intensity values and surface material properties [7]. Group 3: Framework Components - The inverse rendering framework includes a relightable scene representation that supports decoupled and joint modeling of geometry, material, and lighting [10]. - It utilizes 3D Gaussian splats to represent scene geometry and color, incorporating physical material properties for realistic lighting interactions [13]. Group 4: Experimental Results - Quantitative results show that InvRGB+L significantly outperforms existing methods like UrbanIR in relighting tasks on the Waymo dataset, achieving a PSNR of 30.42 compared to UrbanIR's 28.84 [17][18]. - The model also demonstrates effective LiDAR intensity modeling, achieving an average intensity-RMSE of 0.063, outperforming other methods [19][20]. Group 5: Qualitative Results - Qualitative comparisons reveal that InvRGB+L effectively separates shadows from reflectance, resulting in smoother reflectance estimates compared to UrbanIR and FEGR [22]. - The model showcases versatility in scene editing, including relighting and object insertion, with seamless integration of inserted elements into the environment [23]. Group 6: Limitations and Future Work - Despite its advancements, InvRGB+L has limitations, such as potential inaccuracies in shadow rendering due to the opaque nature of Gaussian splats and insufficient handling of complex nighttime environments [26].

Core Viewpoint - The article discusses the potential of the VLA (Vision-Language-Action) architecture in autonomous driving, emphasizing its long-term viability and alignment with human cognitive processes [2][12]. Summary by Sections VLA Architecture and Technical Potential - VLA has strong technical potential, transitioning from manual to AI-driven autonomous driving, and is expected to support urban driving scenarios [2]. - The architecture is inspired by robotics and embodied intelligence, suggesting it will remain relevant even after the proliferation of robots [2]. Performance Metrics and Chip Capabilities - The Thor-U chip currently operates at 10Hz, with potential upgrades to 20Hz or 30Hz through optimizations [2]. - The VLA model is designed to be platform-agnostic, ensuring consistent performance across different hardware [2]. Language Integration and Cognitive Abilities - Language understanding is crucial for advanced autonomous driving capabilities, enhancing the model's ability to handle complex scenarios [2]. - VLA's ability to generalize and learn from experiences is likened to human learning, allowing it to adapt to new situations without repeated failures [2]. Model Upgrade and Iteration - The 3.2B MoE vehicle model has a structured upgrade cycle, focusing on both pre-training and post-training updates to enhance various capabilities [3]. User Experience and Trust - The article highlights the importance of user trust and experience, noting that different user groups will gradually accept the technology [2]. - Future iterations aim to improve driving speed and responsiveness, addressing current limitations in specific scenarios [5][12]. Competitive Landscape and Differentiation - The company is closely monitoring competitors like Tesla, aiming to differentiate its approach through gradual iterations and a focus on full-scene autonomous driving [12]. - VLA's architecture is designed to support unique product experiences, setting it apart from competitors [13]. Safety Mechanisms - The AEB (Automatic Emergency Braking) function is emphasized as a critical safety feature, ensuring high frame rates for emergency scenarios [14].

Financial Data and Key Metrics Changes - The company reported a record revenue of $50 billion for the second quarter, with adjusted EBIT of $2.1 billion, reflecting a year-over-year improvement in costs excluding tariffs [7][32] - The full-year adjusted EBIT guidance has been updated to a range of $6.5 billion to $7.5 billion, net of tariffs [7][39] - Adjusted free cash flow was solid at $2.8 billion, with a strong balance sheet showing over $28 billion in cash and $46 billion in liquidity [36][37] Business Line Data and Key Metrics Changes - Ford Pro's revenue grew 11% to nearly $19 billion, with an EBIT margin of 12.3%, driven by a strong product lineup and high-margin services [33] - Model E revenue more than doubled to $2.4 billion, with a significant margin improvement of nearly 44 points [34] - Ford Blue earned nearly $700 million in the quarter, reflecting profitable market share gains and higher net pricing [35] Market Data and Key Metrics Changes - In the U.S., Ford's sales grew 7 times faster than the industry, with market share up 1.7 points sequentially [19] - The company sold more electrified vehicles than its two main domestic rivals combined, with EVs and hybrids making up close to 14% of the U.S. mix [20] - Outside the U.S., Ford gained market share in key markets such as Canada, Europe, South America, and the Middle East [22] Company Strategy and Development Direction - The company is shifting capital towards Ford Pro, reallocating resources from future EV programs to accelerate growth in high-margin services [9] - Ford aims to enhance its product lineup with a focus on trucks and iconic products, while also investing in low CO2 emissions technologies [13][14] - The company is committed to improving vehicle quality, with expectations of declining warranty costs in the coming years [15] Management's Comments on Operating Environment and Future Outlook - Management acknowledged the impact of tariffs, estimating a net headwind of about $2 billion for the year, while expressing confidence in the company's cycle plan [12][39] - The management highlighted the importance of a durable national emission standard to ensure sound industry planning and reduce compliance costs [14] - The company is optimistic about its ability to navigate the changing regulatory environment and capitalize on opportunities in the EV market [66][70] Other Important Information - The company announced a regular dividend of 15 cents per share, payable on September 2, reflecting its commitment to return capital to shareholders [38] - Ford's industrial platform is focused on cost and quality improvements, targeting a net improvement of $1 billion this year, excluding tariffs [25][32] Q&A Session Summary Question: Drivers of guidance change and improvement - Management explained that the guidance reflects strong business improvement despite absorbing larger tariffs, with a focus on sustainable cost improvements [42][44] Question: Strategic spending on EV side - Management indicated a shift in EV spending and capital allocation, emphasizing flexibility in powertrain options and reallocating resources to Ford Pro [48][50] Question: Recall issues and warranty coverage - Management acknowledged improvements in warranty coverage but noted that FSAs have a longer arc, with early indicators showing lower costs for newer model years [56][58] Question: Market share sustainability - Management expressed confidence in sustaining market share gains into the second half of the year, despite expectations of a softer market [60][62] Question: Balancing emissions policy and EV competitiveness - Management highlighted the importance of changing emissions policies as a tailwind for the business, while also focusing on competitive EV strategies [66][70] Question: Tariff negotiations and outcomes - Management discussed ongoing productive conversations with the administration regarding tariff simplification and potential reductions [82][84]

创新探索"公交+医疗"服务模式。5月，无锡在江大附属医院南院区上线全市首个医院版"巴士邻居"，市 民就诊完毕，便可直接走进门诊大楼门前由公交车改造的休憩驿站，通过"无锡云公交"小程序花3元预 约"动态公交"回家。目前，"巴士邻居"订单每天保持在40个左右，"动态公交"日均接送就医市民约50人 次。以"驿站+叫车"组合模式，无锡正打通从"家门"到"院门"公共出行"最后100米"。 本报讯(记者李顺顺)7月10日，全国首条开进医院的智驾公交线在无锡正式上线。作为1.8公里就医专 线，公交微巴43号线(江南大学附院智驾线)精准对接地铁出站口至医院门诊楼，实现市民公共交通出 行"一站就医"。 公交微巴43号搭配了全套硬件智能设备及后台高阶算法，具备L4级驾驶能力的自动驾驶。该线路从地 铁长广溪站出发，途经震泽路、蠡湖大道等，进入江大附属医院内部，全程约5分钟，真正做到一站就 医且无需预约，有需要的市民可在站台直接上车，试运行期间暂不收费。 ...

近年来，人工智能（AI）已成为创投机构竞相布局的核心赛道。进入2025年，一级市场的AI投资逻辑 发生了怎样的变化？带来哪些新的投资机会？AI在垂域领域的应用百花齐放，最有望在什么样的细分 赛道诞生新的超级独角兽？近日，在第十三届创业投资大会暨全国创投协会联盟走进光明科学城活动 的"人工智能投资新机遇"圆桌论坛上，一线投资人从自身视角出发，给出了各自的观察。 从模型到应用 AI投资呈现新变化 "随着科技的发展曲线越来越陡峭，很多行业和公司呈现'出道即巅峰'的情况，包括估值涨得也很快， 留给投资人深入观察和决策的时间非常短。"TCL创投管理合伙人马华说，这要求投资人有更敏锐的洞 察力、判断力和决策力。 周波认为，作为投资机构，要真正抓住这波机遇并能挑选到好的项目，需要做到以下几点：第一，保持 好自身的定力，练好内功，深耕产业，把产业逻辑和投资逻辑真正搞清楚，而不是盲目追逐热点；第 二，需要更加关注团队，尤其是投一些早期项目时，团队是最为核心的。拉长周期来看，决定企业能不 能跑出来，关键的因素还是行业趋势和核心团队。从过去的经验看，"技术大牛+领域专家+产品经 理"这样的配置会比较好；第三，要贴近产业尤其是重视对 ...