群友底稿5697字，TOP2将其压缩为1270字： 原先开宝马X5，首款电车是两年前的38万顶配腾势N7（吸引点认为比亚迪技术雄厚/腾势是高端品 牌/D9是爆款/双腔空悬/双腔充电/天神之眼），但因体验未达预期，开几个月后便亏十几万卖出。 此前对理想的印象是500万最好的SUV/崴脚/增程器笑话，到店体验i8展车后，认为i8有 精美的方向 盘，工工整整的双联屏（其中双联屏是重要决策点，认为好看/完美融入中控台/屏幕大小合适/主 副驾都能看），精致的空调出风口，好看的门侧板设计，氛围灯等等。舒适度好像也还行，冰箱 电视蛮得劲，音响听着还行，车机流畅等优点，看完后决定购买。 不针对理想，本身买车主要考虑安全、好看、预算、功能（智驾/车机/座椅/娱乐/冰箱/便捷）。 提车后的实际使用中，超出展厅体验的优点： 舒适性: 座椅和底盘的舒适度超出预期，优于原先的判断，NVH表现安静。 智能化: 车机对话流畅，便利性高；智驾系统在长途高速中表现出色，显著降低驾驶疲劳。 补能: 体验了5C充电，补能速度快，十分钟即可完成。 i8的综合体验远超2年前同价位购入的腾势N7，很高满意度。 对现有产品的批判性感受 认为理想又有点不够 ...

视频版： 压缩版： 时睦华认为理想L9像苹果，注重高品质、无感的细节优化和人机工程。问界M8则像华为，注重炫 酷、有感的科技功能。 问界M8 Ultra做得更好的地方： 第二排零重力座椅： 这是M8的完胜项。座椅会横向移动以避开轮拱，实现更大的躺倒角度，配合专 用小扶手和集成在座椅上的安全带，舒适度极高，且可在行驶中使用。 炫酷科技体验： M8提供了更强的感官刺激。例如可隔空手势开启的电动门、投影大灯的光毯功能、 AR-HUD等，科技感更强，能刺激消费欲望。 辅助驾驶风格： 两者处于同一梯队，但风格不同。华为的辅助驾驶感觉上通行效率更高，博弈很强 （例如会别车），但舒适度和安全感稍弱。 理想L9 Ultra做得更好的地方： 品质感与做工： L9的内饰用料（如皮革、镀铬）和装配工艺明显更高级。M8在方向盘皮革、按钮、 装饰条等方面被指塑料感强，且老板键松散、座椅调节时有声音。 人机工程设计： 这是L9的巨大优势。 前排座椅： L9的座椅贴合度极高，对背部和腿部承托到位，长途舒适性强于M8（ 时睦华认为 M8的 座椅乘坐舒适度偏低）。 扶手设计： L9的扶手位置合理。 时睦华认为 M8的扶手设计存在硬伤，驾驶员胳 ...

Core Viewpoint - The article emphasizes the strategic vision and collaborative efforts of Li Auto as it celebrates its tenth anniversary, focusing on innovation, partnership, and future growth in the electric vehicle industry [5][20]. Group 1: Event Overview - The 2025 Global Partner Conference was held on October 30, 2025, in Changzhou, with over 600 global partners attending to celebrate the company's achievements and discuss future strategies [5][6]. - The theme of the conference was "Win-Win, Innovation, Nexus," highlighting the collaborative ecosystem that Li Auto aims to build with its partners [2][5]. Group 2: Strategic Insights - Li Auto's management team outlined a strategic roadmap for the next three years, focusing on product development, technological advancements, and supply chain improvements [9][10]. - The company plans to accelerate the iteration speed of its technology platforms and products to maintain a competitive edge in the market [4][10]. Group 3: Technological Innovations - The conference showcased innovations such as AI-driven flexible manufacturing, advanced driver assistance systems, next-generation power batteries, and smart chassis technologies [3][20]. - The "Smart Chain Park" exhibited the collaborative achievements of Li Auto and its partners over the past decade, emphasizing the resilience and innovation in the electric vehicle sector [20][21]. Group 4: Recognition and Awards - A ceremony was held to honor partners with awards for technical contributions, quality excellence, and collaborative achievements, reinforcing the commitment to continued partnership and value creation [27][32]. - The awards symbolize Li Auto's gratitude for past contributions and its vision for future collaboration in the electric vehicle industry [27][32].

以下文章来源于晚点Auto ，作者晚点团队 晚点Auto . 从制造到创造，从不可能到可能。《晚点LatePost》旗下汽车品牌。 本文经授权转自《晚点AUTO》 作者：赵宇 编辑：龚方毅 黄俊杰 42 岁之前，郎咸朋从不抽烟，但在去年夏天理想研发 "端到端" 智驾方案期间，他每个工作日都得 来上两根。 技术的演进常伴随争议，而最终消解争议的仍是产品本身。郎咸朋认为，相比有监督训练的 "端到 端"，无监督训练的 VLA 迭代效率更高，最晚到明年初，外界就能看到明显提升。 相比我们此前两次交流（一年前推出 "端到端" 方案，以及两个月前 VLA 临近落地），郎咸朋这次更松 弛一些，近三个小时的谈话中，他语速平稳、声音轻快。谈及理想智驾的进展和技术选择，他的用词也 更笃定。 见面前不久，理想智驾团队又经历了新一轮架构调整和人员变动。这个 2018 年成立的团队已经换了三 代骨干。作为理想智驾第一号员工，郎咸朋向我们完整回顾了团队的发展演变历程，他加入理想以来的 工作理念和方法，并首次回应了外界对理想新技术的质疑。 以下是访谈及少量追加问答的主要内容，经编辑。灰色引用模块则是我们做的信息补充： 不可能用华为的方式打 ...

Core Viewpoint - The article discusses the future of Li Auto's VLA technology, emphasizing the importance of a reinforced learning loop and the potential for significant advancements in autonomous driving capabilities by 2027 [1][2]. Short-term Outlook - Li Auto aims to establish a reinforced learning loop by the end of 2025, which is expected to enhance user experience significantly, making the vehicle feel more "alive" and responsive [1]. Mid-term Outlook - With the reinforced learning loop in place, Li Auto anticipates surpassing Tesla in the Chinese market due to its advantageous environment for iterative improvements [1]. Long-term Outlook - The VLA technology is projected to achieve Level 4 autonomy, with the expectation of new technologies emerging beyond this milestone [1]. Business Process Transformation - The transition to reinforced learning is not just a technical change but a fundamental business transformation that will create a competitive moat for the company [1][3]. Team Dynamics and Leadership - The restructuring of the autonomous driving team focuses on building a robust business system rather than relying on individual talents, with an emphasis on internal talent development [7][8]. AI and Computational Needs - The current intelligence requirements for driving are considered low, and after the business process reform, clearer insights into computational needs will emerge [3][4]. Competitive Landscape - The article suggests that multiple players will exist in the autonomous driving space, and the narrative of having unique capabilities may not constitute a strict competitive moat [2][8]. Data and Model Development - The importance of data quality and distribution in training models is highlighted, with a focus on addressing corner cases to enhance system performance [9]. Strategic Insights - Li Auto's strategy emphasizes the need for substantial resource allocation and continuous investment in AI technology, akin to the role of Elon Musk at Tesla [8][12]. Organizational Structure - The restructuring of the autonomous driving department includes the formation of various specialized teams to enhance operational efficiency and employee engagement [7][11]. Future Projections - By 2027, the industry may shift away from traditional metrics like MPI, indicating a potential evolution in performance evaluation standards [11].

Core Insights - The article discusses the leadership philosophy of Li Xiang, emphasizing the importance of young talent in research and development, and the unique management styles within the company [1][7][11] Group 1: Leadership Philosophy - Li Xiang believes that experience can be a barrier to research, advocating for a high proportion of fresh graduates in research teams, which currently stands at around 60-70% [1][7] - The company employs different management styles for various teams, including manufacturing, operating systems, and autonomous driving, with a core team of about 200 people dedicated to end-to-end autonomous driving [6][7] - Li Xiang admires Liang Wenfeng's self-discipline and his approach to researching global best practices, which has influenced the company's operational strategies [4][5][11] Group 2: AI and Engineering Insights - Li Xiang expresses confidence in his engineering background, stating that while he may be misled in AI science, he cannot be deceived in AI engineering due to his strong engineering mindset [2][16] - The company has benefited from the open-source project DeepSeek, which accelerated their development timeline for language models by nine months [5][8] - Li Xiang emphasizes the importance of structural questioning in engineering, which aids in improving team efficiency and problem-solving [18] Group 3: Talent Acquisition and Competition - The company is focused on attracting talent by emphasizing its commitment to AI and the importance of real-world applications, which enhances its appeal to potential recruits [10] - Li Xiang notes that while competitors may have larger teams, the company's smaller, focused team has achieved superior product experiences in autonomous driving [6][7] Group 4: Best Practices and Growth - Li Xiang identifies growth as a central theme in his leadership, linking personal development to user value and commercial success [15] - The company aims to internalize best practices, particularly in research and analysis, to enhance success rates in various projects [13][14]

动驾! H 线上直播 特斯拉FSD v14畅聊,有哪些技术值得国内关注? 中 * 世界模型和VLA未来发展方向探讨,是否有可能走 向融合统一? * 数据和算力的高需求导致学术界越来越难以参与智 驾的游戏,学术界还有哪些机会? FSD v14 藏了 VLA 吗? 谁在定义自动驾驶下一代方案: WA vs VLA 詹锟 北航自动化硕士,理想汽车 VLA团队高级总监,兼任理 想汽车美国硅谷研发中心的负 责人 连线嘉宾 圆桌正当时 江岸青 早稻田大学博士,博世中央 研究院高级算法科学家, vla/闭环算法 研究team leader 许凌云 中国科学院博士,卡内基梅 隆机器人研究所博士后。现 任长安汽车泊车团队负责人 张志鹏 上海交通大学人工智能学 院PI,博士生导师 主持人 Gloria 自动驾驶之心联创 刘斯坦 知乎大V,深度流光联合创 始人 & CTO | 19:30 扫描二维码免费观看 ...

Research Background and Challenges - The safety and reliability of autonomous driving systems heavily depend on 4D dynamic scene reconstruction, which includes real-time, high-fidelity environmental perception in 3D space plus the time dimension. The industry faces two core contradictions: the limitations of static feedforward solutions, which assume "no dynamics in the scene," leading to severe artifacts when encountering moving targets like vehicles and pedestrians, making them unsuitable for real driving scenarios [1]. Core Innovations - Harbin Institute of Technology, in collaboration with Li Auto and other research teams, has achieved three key design breakthroughs to unify "real-time performance, high fidelity, and multi-task output" [2]. Related Work Overview - Static driving scene reconstruction methods include DrivingForward, pixelSplat, MVSplat, and DepthSplat, which have shown limitations in adapting to dynamic environments [3]. Key Technical Solutions - A two-stage training paradigm is proposed, where a robust static scene prior is learned from large-scale data before training the dynamic module, addressing the instability of end-to-end training and reducing the complexity of dynamic modeling [4]. - A hybrid shared architecture with a residual flow network is designed, featuring a shared depth encoder and a single-camera decoder to predict only the non-rigid motion residuals of dynamic objects, ensuring cross-view scale consistency and computational efficiency [4]. - A pure visual online feedforward framework is introduced, which inputs two consecutive panoramic images to output 3D Gaussian point clouds, depth maps, and scene flows in real-time, meeting the online perception needs of autonomous driving without offline optimization or multi-modal sensors [4]. Experimental Validation and Results Analysis - The method significantly outperforms existing feedforward baselines in quantitative results, achieving a PSNR of 28.76, which is 2.66 dB higher than Driv3R and 2.7 dB higher than DrivingForward, and an SSIM of 0.895, indicating superior rendering fidelity [28]. - The efficiency analysis shows that the proposed method has a faster inference time of 0.21 seconds per frame, which is 38% faster than DrivingForward and 70% faster than Driv3R, with a training cost of approximately 5 days and VRAM usage of 27.3 GB, significantly lower than Driv3R [30]. - Ablation studies confirm the necessity of the residual flow network, two-stage training, and flow distortion loss, highlighting their critical roles in dynamic modeling and rendering quality [32][34].

Core Viewpoints - The article presents eight key viewpoints regarding the performance and evaluation of autonomous driving technologies, particularly focusing on the experiences with Horizon's HSD and Li Auto's VLA systems [2]. Group 1: Performance Evaluation - TOP2 found the Horizon HSD software experience during a 1.5-hour test drive in Hangzhou to be significantly better than the current production version of Li Auto's L7 VLA [2]. - There is a possibility that the production version of Horizon's software may not perform as well as the engineering version experienced during the test [2]. - The evaluation of autonomous driving systems is limited by the number of test experiences, as a few tests cannot generalize performance across different regions [3]. Group 2: Technical Architecture - Horizon employs a VA-style end-to-end system, while Li Auto uses a VLA-style end-to-end system, with the naming being a minor distinction [3][9]. - The current technological landscape suggests that VA-style systems may have advantages in user experience due to existing computational and bandwidth limitations [6]. - Li Auto's decision to adopt a VLA-style system is seen as a courageous move, as it requires significant resources and presents various challenges [14]. Group 3: Market Dynamics - The future landscape of autonomous driving operators is uncertain, with a prevailing belief that only a few companies will survive, particularly those capable of self-developing their technologies [4]. - Companies lacking self-research capabilities in autonomous driving may struggle to adapt to the evolving smart vehicle industry [4]. - The article emphasizes that autonomous driving is not merely a selling point but a differentiating capability that can lead to high market concentration due to low marginal costs [4]. Group 4: User Experience Insights - Feedback from Horizon personnel indicated that the performance of their systems in extreme weather and complex scenarios is generally average, highlighting the need for comprehensive testing [5][6]. - The experiences reported during the test drives varied significantly based on the vehicle models and their respective chip capabilities, indicating that performance can be inconsistent [7]. - The article suggests that the perception of Horizon's HSD performance may be overly positive due to selective testing locations and conditions [8].

Core Viewpoint - The company acknowledges a significant incident involving battery thermal runaway and emphasizes the importance of safety and proactive measures in vehicle management [2][5]. Incident Analysis - The company has delivered over 1.4 million vehicles without any thermal runaway incidents due to external factors, attributing this to robust quality control and an AI-based quality warning system [2]. - Prior to the incident, the cloud system reported a battery insulation fault over four hours before the event, and the vehicle had entered a breakdown state due to battery issues [3]. - The failure to take immediate action despite the warnings is attributed to complacency, as the company had not previously encountered such issues [3]. Cause of the Incident - The root cause of the insulation short circuit was determined not to be the battery cells themselves, but rather corrosion of the aluminum plate due to inadequate coolant protection [4]. - The company recognizes the need for zero tolerance regarding safety risks, even if they are perceived as low probability [4]. Recall Decision - A consensus was reached among company leaders to initiate a recall to replace affected components, prioritizing safety over cost considerations [5]. - The recall process was expedited, with preparations for new battery and motor controller production underway [5][6]. Production Capacity Challenges - The current production capacity for batteries is 3,300 units per month, necessitating suppliers to ramp up production capabilities for the recall [6]. Leadership Involvement - Notably, the company's founder did not participate in the recall decision meetings, indicating a unified commitment to safety among the leadership team [6]. User Communication - The company expresses sincere apologies to users affected by the incident, acknowledging the inconvenience caused [8].