Core Viewpoint - GeoDrive is a next-generation world model system for autonomous driving, developed collaboratively by Peking University, Berkeley AI Research (BAIR), and Li Auto, addressing the limitations of existing methods that rely on 2D modeling and lack 3D spatial perception, which can lead to unreasonable trajectories and distorted dynamic interactions [11][14]. Group 1: Key Innovations - **Geometric Condition-Driven Generation**: Utilizes 3D rendering to replace numerical control signals, effectively solving the action drift problem [6]. - **Dynamic Editing Mechanism**: Injects controllable motion into static point clouds, balancing efficiency and flexibility [7]. - **Minimized Training Cost**: Freezes the backbone model and employs lightweight adapters for efficient data training [8]. - **Pioneering Applications**: Achieves real-time scene editing and VLA (Vision-Language-Action) collaborative planning within the driving world model for the first time [9][10]. Group 2: Technical Details - **3D Geometry Integration**: The system constructs a 3D representation from single RGB images, ensuring spatial consistency and coherence in scene structure [12][18]. - **Dynamic Editing Module**: Enhances the realism of multi-vehicle interaction scenarios during training by allowing flexible adjustments of movable objects [12]. - **Video Diffusion Architecture**: Combines rendered conditional sequences with noise features to enhance 3D geometric fidelity while maintaining photorealistic quality [12][33]. Group 3: Performance Metrics - GeoDrive significantly improves controllability of driving world models, reducing trajectory tracking error by 42% compared to the Vista model, and shows superior performance across various video quality metrics [19][34]. - The model demonstrates effective generalization to new perspective synthesis tasks, outperforming existing models like StreetGaussian in video quality [19][38]. Group 4: Conclusion - GeoDrive sets a new benchmark in autonomous driving by enhancing action controllability and spatial accuracy through explicit trajectory control and direct visual condition input, while also supporting applications like non-ego vehicle perspective generation and scene editing [41].

Core Viewpoint - The removal of the capacitive steering wheel in the 25th version signifies advancements in driver monitoring technology, enhancing safety by relying more on visual detection systems rather than solely on capacitive sensors [1][6]. Summary by Sections Capacitive Steering Wheel - The capacitive steering wheel detects driver engagement by measuring changes in capacitance when hands are placed on it, serving as a critical input for the Driver Monitoring System (DMS) [2][6]. - The need for a driver attention monitoring system arises from the misuse of assisted driving features, which can lead to dangerous behaviors such as sleeping or using mobile devices while driving [2][6]. Detection Technologies - Various detection methods include: 1. Torque detection, which measures the force applied to the steering wheel [2][3]. 2. Capacitive detection, which relies on the presence of hands on the steering wheel [2][4]. 3. Camera detection, which monitors the driver's eye state [3][4]. - Each method has its limitations, necessitating a combination of techniques to improve accuracy and reduce the potential for deception [4][5]. Changes in the 25th Version - The 25th version has removed the capacitive steering wheel, reflecting technological advancements that allow for more reliable visual detection systems [1][6]. - The previous combination of "torque + capacitive + camera" has been simplified to "torque + camera," making it easier for drivers to comply with monitoring requirements [6]. - The evolution of technology has reduced reliance on hardware components, enhancing the overall user experience while maintaining safety standards [6].

Group 1 - The core viewpoint of the article highlights the progress in the construction of supercharging stations, with a total of 2,428 stations built, achieving 90.69% of the target for 2,500+ stations by the i8 release date [1] - The remaining time until the i8 release is 53 days, requiring an average of 1.36 new stations to be built daily to meet the target [1] - For the year-end goal of over 4,000 stations by 2025, the current progress stands at 30.84%, with 206 days left in the year, necessitating the construction of 7.63 stations per day [1] Group 2 - The newly constructed station is located in Shanghai, specifically in the Pudong New Area, and is categorized as a 4C station with specifications of 4C × 4 [1]

独立摄像头是800万像素的，"片源"清晰。 LCD屏幕也是高清的，分辨率和我们中控屏一样， PPI达到了212。 刷新帧率也达到了50帧每秒。 第二：视场角特别大， 120度，其他70度 80度的摄像头能看到3车道的时候，这块后视镜可以看到5车道，几乎没有视野盲区了。 而且120°视野 角与人眼的主要视野一致，就跟最好的FPS游戏也必须采用120°一样，当人眼的视线在内后视镜和车风挡前方 道路切换，不会造成人眼的不适应。 第三：防眩目，防反光。 双重防眩目，摄像头防曝光，LCD 屏幕防眩目。 同时屏幕还专门做了防反光的设计，避免把车内的场景反光到LCD屏幕上。 但其 实，我们也不是一开始就是做出了这么一个流媒体后视镜的，中间也经历了无数的波折。有不少小故事，好 的产品其实并不是一蹴而就。 最开始,第一个问题是，到底要不要给L9单独做一块流媒体后视镜？ 由于21寸屏幕的引入，当二排观影的时候，屏幕放下，物理后视镜就被全部遮挡了。 这个是真实的场 景，但是，到底要不要为此而就单独来做一块流媒体后视镜呢？因为毕竟要背很高的成本，到底有没有足够 的用户价值？ 这里面其实是有很多不同意见的，很抱歉，我当时属于智能焕新版 ...

本文经过认真思考，有任何不同意见都可在评论区留言，我都会回复的。 看待一个东西的角度有非常多种，当一个人说XX可以和XX类比时，一般是某一个角度有相似之处， 任意两个事物不太可能所有细节都可以类比。 理想VLA和DeepSeek MoE( 混合专家 )类比点: VLA和MoE本身的想法都有其他人先提过了，都是首次完整落地到另一个大领域，在其中有大量创 新，并取得良好结果。 理想暂时还没有自己的MLA( 多头潜在注意力机制 )，之后会有的。DeepSeek的MLA创新尺度是这 个方法的理念之前没人提过。 DeepSeek之前的MoE，专家数量一般只有8-16个，单个专家需处理多种类型知识，专业化程度低，不 同专家重复学习相同公共知识，参数利用率低。 DeepSeek的MoE核心通过 Fine-Grained Expert Segmentation( 细粒度专家划分)和 Shared Expert Isolation( 共享专家隔离)，处理方式和原来的MoE已经非常不同了。 前者将单个专家拆分为更小的子专家（原专家隐藏层维度缩小至 1/4，数量增至4倍），让激活专家 组合灵活性显著提升（从120种组合的数量级增至 ...

Core Viewpoint - The article discusses the rising popularity of the "Ideal Classmate" toy IP, particularly among male consumers aged 28-50 in key Chinese regions, and highlights the potential for community building through this IP [1][7]. Group 1: Consumer Insights - The "Ideal Classmate" toy IP has received positive feedback, especially from males aged 35-45, indicating a shift in consumer perception [1]. - There is a notable interest among consumers in using the "Ideal Classmate" for emotional connection rather than functionality, aligning with the preferences of younger generations [4]. Group 2: Brand Strategy - The conversation reveals that the founder, Li Xiang, is learning from the success of Pop Mart, emphasizing the importance of emotional value over functional features in toy design [2][4]. - Li Xiang suggests that the evolution of IP should focus on community identity, as consumers seek to express their belonging through products [6]. Group 3: Market Positioning - The "Ideal Classmate" has the potential to create a community recognition platform through various forms such as physical toys, in-car systems, and mobile applications [7]. - Feedback indicates that the "Ideal Classmate" has a stronger conversational capability compared to competitors, enhancing its appeal among both children and adults [7].

加微信，进群深度交流理想长期基本面。不是车友群。 来源： 北北自律机 25年06月07日星期六 理想超充 3 新增。 超充建成数：2424→2427座 ———————————————————— 基于i8发布日期 2500+座目标 新增数进度值：90.17%→90.56% i8发布剩余54天（按7月31假 设） i8发布剩余时间进度值：74.41% 需每日 1.35 座，达到 i8发布 目标值 基于2025年底 4000+座目标 今年新增数进度值：30.66%→30.80% 今年剩余207天今年时间进度值：43.29% 需每日 7.60 座，达到年底目标值 【附】3 座新增建成 海南省 琼海市 琼海高铁站停车场 为城市枢纽4C站，规格：4C × 6 福建省 泉州市 泉州丰泽刺桐北拓 为城市4C站，规格：4C × 8 浙江省 杭州市 杭州千岛湖诺富特酒店 为城市景区4C站，规格：4C × 6 ...

Core Viewpoint - The article discusses the advancements in the AD Max driver agent product, focusing on its capabilities in closed park and underground garage scenarios, emphasizing multi-modal information integration for decision-making. Group 1: Product Definition and Capabilities - The AD Max driver agent has achieved full model-based trajectory output, differing significantly from previous AVP product experiences, providing a driving experience that closely resembles that of human drivers in specific environments [1] - The agent can understand road signs and engage in voice interactions, utilizing both local multi-modal LLM for simple commands and cloud-based large-scale LLM for complex instructions [1][2] - The agent builds associative points rather than precise maps, allowing it to navigate based on general driving structures, similar to human behavior in underground garages [2] Group 2: Perception and Reasoning Abilities - The AD Max agent integrates data from various sensors, including cameras and LiDAR, to achieve comprehensive environmental perception capabilities [2] - The agent demonstrates the ability to remember associative points, enabling it to navigate without needing to roam through the area again, and can adapt if the memory is incorrect [3] Group 3: Industry Comparison - The AD Max driver agent and NIO AD's NWM are highlighted as the only two applications currently integrating multi-modal perception information into a single model for complex reasoning [3]

理想实时语音对话大模型MindGPT-4o-Audio上线，作为全模态基座模型MindGPT-4o的预览preview版 本，MindGPT-4o-Audio是一款全双工、低延迟的语音端到端模型，可实现像人类一样"边听边说"的自 然对话，并在语音知识问答、多角色高表现力语音生成、多样风格控制、外部工具调用等方面表现突 出，达到了媲美人人对话的自然交互水平。 核心功能 目前，基于MindGPT-4o-Audio的理想同学已在理想车机及理想同学手机App全量上线。 1. 模型能力 1.1 整体算法方案 MindGPT-4o-Audio是一款级联式的语音端到端大模型，我们提出了感知-理解-生成的一体化端到端流式 生成架构实现全双工、低延迟的语音对话。其中： 在各项权威音频基准测试以及语言理解、逻辑推理、指令遵循等语言理解任务上，MindGPT-4o-Audio 已达到行业领先水平，在语音交互评测基准VoiceBench多类评测中均显著领先行业领先的同类模型。此 外，我们实验发现，业内主流的语音端到端模型一般会在提升语音交互能力的同时，造成语言交互能力 的大幅下降，MindGPT-4o-Audio通过训练策略的优化保 ...

基于i8发布日期 2500+座目标 新增数进度值：89.65%→90.17% i8发布剩余55天（按7月31假设） i8发布剩余时间进度值：73.93% 需每日 1.38 座，达到 i8发布 目标值 基于2025年底4000+座目标 今年新增数进度值：30.49%→30.66% 今年剩余208天 今年时间进度值：43.01% 需每日 7.58 座， 达到年底目标值 【附】4 座新增建成 广东省 汕尾市 汕尾豪通车行北侧停车场 为城市4C站， 规格：4C × 6 山东省 济南市 济南槐荫汽车西站整备基地 为城市4C站，规格：4C × 6 天津市 东丽区 天津海月道 为城市4C站，规格：4C × 6 浙江省 宁波市 宁波新府银座 为城市4C 站，规格：4C × 8 加微信，进群深度交流理想长期基本面。不是车友群。 来源： 北北自律机 25年06月06日星期五 理想超充 4 新增。 超充建成数：2420→2424座 ———————————————————— ...