新浪科技讯 1月8日下午消息，在今日的一场沟通会上，谈及新能源汽车销售承压的问题，小鹏汽车董 事长、CEO何小鹏向《次世代车研所》等表示，"今天的变化是片刻的，（行业）过段时间会恢复，无 需担心，最坏的时候也是最好的时候，小鹏安心做好VLA、VLM、人形机器人量产等，相信今年对小 鹏来说存在巨大机会。" 责任编辑：石秀珍 SF183 新浪科技讯 1月8日下午消息，在今日的一场沟通会上，谈及新能源汽车销售承压的问题，小鹏汽车董 事长、CEO何小鹏向《次世代车研所》等表示，"今天的变化是片刻的，（行业）过段时间会恢复，无 需担心，最坏的时候也是最好的时候，小鹏安心做好VLA、VLM、人形机器人量产等，相信今年对小 鹏来说存在巨大机会。" 责任编辑：石秀珍 SF183 ...

以下文章来源于红色星际 ，作者红色星际科技 红色星际 . 让更多人，更深入地了解自动驾驶行业！ 作者 | 钟声 来源 | 红色星际 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 2025年结束了，2026年马上到来。 2025年对于智驾人来说仍旧是非常辛苦的一年，为了技术攻关和量产交付节点，没少过封闭开发的日子。不过，回顾2025，这是智驾承上启下和辞旧 迎新的一年。 2025年智驾最主要的两条线是：向下普及和向上挑战。 比亚迪、吉利、奇瑞等传统车企扮演了向下普及的角色，掀起了全民智驾的浪潮，把中阶的高速NOA功能下放到10W+车型上，26年将会继续推进智 驾普及，把城市NOA功能下放到10W+车型上。 新势力以及头部智驾供应商则是在挑战智驾技术的上限，秉持着一年一代新技术的做法，在端到端之后继续探索新技术，引领技术迭代。 可以说，25年主机厂分化成了两个阵营，一个是负责向下普及的传统车企；一个是负责向上挑战的新势力。 2025年，智驾开始挑战技术"深水区"，核心问 ...

Group 1 - The core achievement of GESON Technology in the 2025 Shenzhen Intelligent Robot Dexterous Hand Competition, winning the championship by setting a new limit for long-range delivery tasks, is supported by RoboSense's VLA model and advanced sensor systems [1][3][9] - The competition showcased the industry's leading capabilities in robot-eye coordination technology, emphasizing the commercial strength of creating industrial value through collaboration [3][9] - The event attracted 53 high-level teams from various regions, highlighting the significance of the competition in the context of the "robot mass production year" [9] Group 2 - RoboSense's recent video release demonstrated the robot's ability to perform complex tasks, indicating the integration of its core technologies aimed at flexible automation in delivery, manufacturing, and logistics [5][13] - The competition tested robots under real-world conditions, including human traffic and elevator sharing, emphasizing the challenges faced in the last 100 meters of delivery [11][13] - The success of GESON Technology illustrates the potential for RoboSense's AI robot technology to support autonomous completion of complex tasks, establishing a comprehensive technological barrier from foundational technology to application [11][13]

Core Insights - The robotics field is still in its early stages, as highlighted by Jim Fan, NVIDIA's robotics head, indicating a lack of standardized evaluation metrics and the disparity between hardware advancements and software reliability [1][8][11]. Group 1: Hardware and Software Disparity - Current advancements in robotics hardware, such as Optimus and e-Atlas, outpace software development, leading to underutilization of hardware capabilities [14][15]. - The need for extensive operational teams to manage robots is emphasized, as they do not self-repair and face frequent issues like overheating and motor failures [16][17]. - The reliability of hardware is crucial, as errors can lead to irreversible consequences, impacting the overall patience and scalability of the robotics field [18][19]. Group 2: Benchmarking Challenges - The lack of consensus on benchmarking in robotics is a significant issue, with no standardized hardware platforms or task definitions, leading to everyone claiming to achieve state-of-the-art (SOTA) results [20][21]. - The field must improve reproducibility and scientific standards to avoid treating them as secondary concerns [23]. Group 3: VLA Model Insights - The Vision-Language-Action (VLA) model is currently the dominant paradigm in robotics, but its reliance on pre-trained Vision-Language Models (VLM) presents challenges due to misalignment with physical world tasks [25][49]. - The VLA model's performance does not scale linearly with VLM parameters, as the pre-training objectives do not align with the requirements for physical interactions [26][51]. - Future VLA models should integrate physical-driven world models to enhance their ability to understand and interact with the physical environment [50]. Group 4: Data Importance - Data plays a critical role in shaping model capabilities, with the need for diverse data sources and collection methods being highlighted [31][43]. - The emergence of new hardware and data collection methods, such as Generalist and Egocentric-10K, demonstrates the growing importance of data in the robotics field [36][42]. - The current data collection strategies remain open-ended, with various approaches still being explored [43]. Group 5: Industry Trends - The robotics industry is projected to grow significantly, from $91 billion currently to $25 trillion by 2050, indicating a strong future potential [57]. - Major tech companies, excluding Microsoft and Anthropic, are increasingly investing in robotics software and hardware, reflecting the sector's attractiveness [59].

Core Viewpoint - The robotics field is still in its early stages, with significant advancements in hardware but limitations in software reliability and performance [1][12]. Group 1: Hardware and Software Dynamics - Current hardware advancements outpace software development, leading to reliability issues that hinder software iteration speed [11][14]. - Many demonstrations of robotic capabilities are often the result of selecting the best performance from numerous attempts, rather than consistent reliability [7][22]. - The need for extensive operational teams to manage robots highlights the challenges in hardware reliability, including overheating and motor failures [18][19]. Group 2: Benchmarking Challenges - The robotics sector lacks standardized benchmarks, making it difficult to assess performance consistently across different hardware platforms and tasks [21][22]. - The absence of consensus on evaluation criteria leads to a situation where every new demonstration can be considered state-of-the-art, complicating progress in the field [22][23]. Group 3: VLA Model Limitations - The Vision-Language-Action (VLA) model, currently a dominant paradigm, faces structural issues as it is primarily optimized for visual question answering rather than physical task execution [24][50]. - The performance of VLA models does not improve linearly with the increase in VLM parameters due to misalignment in pre-training objectives [26][52]. - A shift towards video world models is suggested as a more suitable pre-training target for robotics, as they inherently encode physical dynamics [27][53]. Group 4: Importance of Data - Data plays a crucial role in shaping model capabilities, and the integration of hardware and data is essential for effective robotic performance [31][32]. - Recent advancements in hardware, such as Figure03 and others, demonstrate improved motion capabilities, but challenges remain in enhancing hardware reliability [35][37]. - The Generalist model illustrates the scaling law in embodied intelligence, where larger datasets lead to better task performance [38][41]. Group 5: Future Trends and Market Potential - The robotics industry is projected to grow from $91 billion to $25 trillion by 2050, indicating significant investment potential [60]. - Major tech companies are increasingly investing in robotics software and hardware, reflecting the sector's attractiveness despite current challenges [62].

全新蓝山智能进阶版搭载的Coffee OS 3.4系统，融合AI主动服务与人性化交互，构建了一个好聪明、好 看、好用、好玩、好听的"五好座舱"。在23扬声器，支持7.1.4全景声；后排17.3英寸3K娱乐屏打造移动 影院之外，还支持AI多屏扩画、晕动舒缓显示等功能，并特别开发"晕动舒缓显示"功能，行车途中看屏 幕，能有效缓解因感官冲突导致的晕车不适感，体现科技对用户的真实关怀和实用价值。还有"小魏同 学"能主动感知环境与乘客，并提供贴心的协同服务。 VLA+Hi4，智能决策与高效执行的深度融合，实现了从"感知—决策—控制"的全链路协同与能力互 补。二者就像赛车运动中天衣无缝的黄金搭档：VLA作为"领航员"负责理解、预判与规划；Hi4作为"赛 车手"负责精准、稳定与高效执行，两者协同构建了 "主动规避" + "主动稳定" 的双重安全防线，实现了 1+1＞2的智慧出行体验。这种能力在真实出行场景中体现得尤为明显。从城市通勤的突发规避，到恶 劣天气的稳控防滑，再到非铺装路面的从容通过，"领航员"VLA与"赛车手"Hi4的毫秒级联动，构成了 全场景智慧安全的核心。 商报讯（记者 郭雨昕）12月22日，魏牌全新蓝山智能 ...

Core Viewpoint - The article discusses the rapid growth and market dynamics of urban NOA (Navigation on Autopilot) suppliers, highlighting the strategic investments and partnerships that are shaping the industry landscape [4][5]. Group 1: Investment and Market Position - Yuanrong has secured strategic investments from leading Tier 1 suppliers and luxury car manufacturers, indicating strong industry interest in high-quality urban NOA suppliers [4]. - Major players like Huawei, Yuanrong, and Momenta each hold over one million urban NOA project orders, suggesting a competitive market structure [5]. Group 2: Growth and Market Trends - Yuanrong has delivered 200,000 vehicles equipped with urban NOA, achieving a nearly 40% market share in the third-party supplier market by October 2025 [4]. - The urban NOA market is expected to experience significant growth, surpassing highway NOA as the mainstream solution due to the increasing adoption and technological advancements [4][6]. Group 3: Future Projections and Challenges - By 2026, urban NOA is projected to see a major surge in volume, driven by reduced hardware costs and the integration of intelligent driving in traditional fuel vehicles, potentially adding millions of units to the market [6]. - Achieving a production scale of over one million units will be a critical milestone for leading intelligent driving companies, as it will help establish data barriers and competitive advantages [6][7]. Group 4: Technological Evolution - The article emphasizes the importance of technological iteration, particularly the transition from VLA (Vehicle Level Automation) from initial production to significant performance improvements in 2026 [7]. - Companies must balance the need for cost-effective urban NOA solutions with advancements in cutting-edge technologies to remain competitive in the evolving market [8].

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 最近自动驾驶的两大前沿方向：VLA和世界模型，已经有明显的融合趋势 。这一想法是十月份看到中科院的DriveVLA-W0，因此笔者借这个机会分别调研了 VLA 和 World Model 相关的工作，并且思考一下 这二者结合的可能性。 经过几周的调研、分析，有了些成果和自己的心得，所以也想理一理，分享给自动驾驶之心的小伙伴 们，主要分为以下几个部分： 那么，这两个看似不同的技术路线，究竟哪条路线更胜一筹？它们是对手，还是最终会携手并进的伙伴？本文将给大家深度解析。首先，咱们聊一聊二者分别是什 么。因为自动驾驶之心平台有发过这两个路线的详解，这里笔者就 high level 的概括一下，感兴趣地小伙伴可以翻翻之前的文章，讲地更为详细。 关于VLA和世界模 型的更多探讨，后面也会在「自动驾驶之心知识星球」内部和大家展开...... 什么是 VLA？ VLA ，全称 Vision-Language-Action， 即"视觉-语言-行动"模型 。 是一个多模态大脑， ...

Key Points - Xiaopeng Motors' founder He Xiaopeng made a bet with his team regarding the performance of their VLA2.0 compared to Tesla's FSD by 2026, indicating confidence in the advancement of autonomous driving technology [4][5] - Nvidia responded to allegations that Chinese startup DeepSeek used smuggled Blackwell chips for AI model training, stating they have seen no evidence of such activities [7] - ZTE Corporation announced its commitment to anti-corruption and is currently in communication with the U.S. Department of Justice regarding compliance investigations related to overseas bribery [9][10] - Zhu Xiaohu commented on Tencent's cautious investment strategy over the past 20 years, emphasizing that the company waits for market clarity before making significant moves [11] - The Chinese government is expected to continue its "national subsidy" policy for consumer goods in 2024, with a focus on optimizing implementation by 2026 [19][20] - MiniMax and Zhizhu, two domestic AI unicorns, are reportedly planning to conduct IPOs in Hong Kong soon, aiming to become the first publicly listed company in the large model sector [21] - JD Industrial, a subsidiary of JD Group, officially listed on the Hong Kong Stock Exchange, raising approximately HKD 2.827 billion [22][23] - Meitu's CEO announced an internal venture initiative, allowing employees to apply for funding to develop AI projects, aiming to enhance organizational efficiency [24] - Lantu Motors' chairman emphasized the need for a breakthrough in the luxury car market, which has been dominated by foreign brands [25] - Xiaomi launched its first self-produced central air conditioning unit at its Wuhan smart home appliance factory, showcasing advancements in its manufacturing capabilities [26][27]

郎咸朋还分享了公司CEO李想的观点，李想认为，未来五到十年，具身机器人核心将有两种形态：汽车 类的具身机器人、人形类的具身机器人。理想的VLA不仅服务于现在的理想各类汽车产品形态，也将 服务于未来的汽车类具身机器人。 凤凰网科技讯12月10日，理想自动驾驶负责人郎咸朋发布长文，回应了宇树科技创始人王兴兴对VLA 的一些担忧。在今年8月的2025年世界机器人大会上，王兴兴表示当下火热的VLA模型(视觉-语言-动作) 是"相对比较傻瓜式的架构"，并表示"保持比较怀疑的态度"。 郎咸朋表示，空谈架构不如看疗效。在自动驾驶领域，脱离了海量真实数据谈模型架构都是空中楼阁， 我们之所以坚持VLA，是因为我们拥有数百万辆车构建的数据闭环，这让我们能在当前算力下，把驾 驶水平做到接近人类。 郎咸朋认为，经过两个月多的"实践出真知"后，VLA就是自动驾驶最好的模型方案，具身智能最终拼的 是整体的系统能力。 ...