Core Viewpoint - The article discusses the advanced technologies used in Li Auto's autonomous driving solutions, specifically focusing on the "MoE + Sparse Attention" efficient structure that enhances the performance and efficiency of large models in 3D spatial understanding and reasoning [3][6]. Group 1: Introduction to Technologies - The article introduces a series of posts that delve deeper into the advanced technologies involved in Li Auto's VLM and VLA solutions, which were only briefly discussed in previous articles [3]. - The focus is on the "MoE + Sparse Attention" structure, which is crucial for improving the efficiency and performance of large models [3][6]. Group 2: Sparse Attention - Sparse Attention limits the complexity of the attention mechanism by focusing only on key input parts, rather than computing globally, which is particularly beneficial in 3D scenarios [6][10]. - The structure combines local attention and strided attention to create a sparse yet effective attention mechanism, ensuring that each token can quickly propagate information while maintaining local modeling capabilities [10][11]. Group 3: MoE (Mixture of Experts) - MoE architecture divides computations into multiple expert sub-networks, allowing only a subset of experts to be activated for each input, thus enhancing computational efficiency without significantly increasing inference costs [22][24]. - The article outlines the core components of MoE, including the Gate module for selecting experts, the Experts module as independent networks, and the Dispatcher for optimizing computation [24][25]. Group 4: Implementation and Communication - The article provides insights into the implementation of MoE using DeepSpeed, highlighting its flexibility and efficiency in handling large models [27][29]. - It discusses the communication mechanisms required for efficient data distribution across multiple GPUs, emphasizing the importance of the all-to-all communication strategy in distributed training [34][37].

Core Viewpoint - The launch of Li Auto's second pure electric model, the Li i8, represents a significant step in the company's pursuit of its "pure electric dream," with a focus on enhanced performance and advanced technology [1][3]. Group 1: Product Launch and Features - The Li i8 is officially on sale as of July 29, with three versions priced between 321,800 yuan and 369,800 yuan, approximately 30,000 yuan lower than the previous pre-sale price [1][3]. - The i8 features longer pure electric range, lower drag coefficient, and the introduction of the MindVLA autonomous driving architecture, which has been in development for years [3][14]. - The i8's dimensions are 5085mm in length, 1960mm in width, and 1740mm in height, with a wheelbase of 3050mm, providing spacious interior comfort [9][11]. Group 2: Competitive Landscape - The i8 enters a competitive market segment for six-seat pure electric SUVs, facing rivals such as the Aito M8, Leapmotor L90, and Tesla Model Y L [4][29]. - The pricing strategy of the i8 is not aggressive, which means it must rely on its overall strength to attract consumers [4][30]. - The market for pure electric models priced above 300,000 yuan is limited, with less than 80,000 units sold in the first four months of 2025, indicating a challenging environment for the i8 [29][30]. Group 3: Strategic Adjustments and Organizational Changes - Following the underperformance of the MEGA model, Li Auto made significant organizational adjustments, merging sales and service teams into a new smart vehicle group to enhance product development [3][21]. - The company has invested approximately 2 billion yuan in design changes for the i8, emphasizing low drag and brand recognition [25][27]. - Li Auto's internal discussions led to a clearer product line strategy, distinguishing the i series from the MEGA brand and focusing on the pure electric SUV market [21][25]. Group 4: Technological Innovations - The i8 is equipped with a self-developed silicon carbide drive motor, achieving a noise level of just 3.5 decibels at high speeds [14]. - The vehicle's dual motor system delivers a combined power of 400 kW (approximately 544 horsepower) and a maximum torque of 660 Nm, with a 0-100 km/h acceleration time of 4.5 seconds [14][15]. - The MindVLA system, a new visual-language-behavior model, allows the i8 to adapt to driving conditions in real-time, enhancing the driving experience [16][18].

Core Insights - The competition among six-seat pure electric SUVs is intensifying, with models like AITO M8, Tesla Model Y L, and Li Auto i8 showcasing unique selling points to capture market share [1][2][3] Group 1: Technology and Features - AITO M8 features the latest HUAWEI ADS4 intelligent driving system, equipped with advanced sensors including a 192-line LiDAR and multiple radar systems, enhancing safety and driving assistance [1] - Tesla Model Y L is recognized for its Autopilot system, which offers extensive driving assistance features, although it faces challenges in fully utilizing its hardware in the domestic market [1][2] - Li Auto i8 is expected to incorporate the next-generation MindVLA driving architecture and NVIDIA's Drive AGX Thor-U chip for advanced data processing and decision-making [2] Group 2: Space and Comfort - AITO M8 offers a spacious design with dimensions of 5190/1999/1795mm and a wheelbase of 3105mm, providing both five-seat and six-seat configurations, along with a 110L front trunk for added convenience [3] - Tesla Model Y L emphasizes minimalist design with a large storage compartment in the center console, facilitating organized storage [3] - Li Auto i8 optimizes space through chassis layout and a multi-layer trunk design, ensuring ample legroom and storage options [3] Group 3: Performance and Range - AITO M8 is built on Huawei's 800V high-voltage battery platform, featuring a 100 kWh battery from CATL, with a maximum CLTC range of 705 km and efficient charging capabilities [3] - Tesla Model Y L offers various range options across different versions, supported by an extensive charging network for both urban commuting and long-distance travel [4]

Core Viewpoint - The article discusses the principles and applications of diffusion models in the context of autonomous driving, highlighting their advantages over generative adversarial networks (GANs) and detailing specific use cases in the industry. Group 1: Diffusion Model Principles - Diffusion models are generative models that focus on denoising, learning and simulating data distributions through a forward diffusion process and a reverse generation process [2][4]. - The forward diffusion process adds noise to the initial data distribution, while the reverse generation process aims to remove noise to recover the original data [5][6]. - The models typically utilize a Markov chain to describe the state transitions during the noise addition and removal processes [8]. Group 2: Comparison with Generative Adversarial Networks - Both diffusion models and GANs involve noise addition and removal processes, but they differ in their core mechanisms: diffusion models rely on probabilistic modeling, while GANs use adversarial training between a generator and a discriminator [20][27]. - Diffusion models are generally more stable during training and produce higher quality samples, especially at high resolutions, compared to GANs, which can suffer from mode collapse and require training multiple networks [27][28]. Group 3: Applications in Autonomous Driving - Diffusion models are applied in various areas of autonomous driving, including synthetic data generation, scene prediction, perception enhancement, and path planning [29]. - They can generate realistic driving scene data to address the challenges of data scarcity and high annotation costs, particularly for rare scenarios like extreme weather [30][31]. - In scene prediction, diffusion models can forecast dynamic changes in driving environments and generate potential behaviors of traffic participants [33]. - For perception tasks, diffusion models enhance data quality by denoising bird's-eye view (BEV) images and improving sensor data consistency [34][35]. - In path planning, diffusion models support multimodal path generation, enhancing safety and adaptability in complex driving conditions [36]. Group 4: Notable Industry Implementations - Companies like Haomo Technology and Horizon Robotics are developing advanced algorithms based on diffusion models for real-world applications, achieving state-of-the-art performance in various driving scenarios [47][48]. - The integration of diffusion models with large language models (LLMs) and other technologies is expected to drive further innovations in the autonomous driving sector [46].

重要行业新闻 1、行业动态 美国对多国征加高额关税，我国已经公布反制关税和非关税壁垒的组合措施 美国总统特朗普3月26日在白宫签署公告，宣布对进口汽车加征25%关税。这一关税措施于4月3日正式生效。符合美加墨协定的汽车零部件暂豁免，直至专门针对此类零部件产 品非美国价值部分征收关税程序出台；4月2日特朗普兑现在美国白宫签署两项关于"对等关税"的行政命令，在这份清单中，中国产品将被加征34%额外关税。叠加此前针对芬 太尼的20%，税率已经涨到至54%。与此同时，包括我国在内的其他国家也在积极制定和调整相应的应对策略。 核心观点 月度产销： 据乘联会初步统计，3月狭义乘用车零售总市场规模约为185.0万辆左右，同比+9.1%，环比+33.7%，其中新能源零售预计可达100万，渗透率回升至54.1%；上险数 据看，3月(3.3-3.30)国内乘用车累计上牌168.01万辆，同比+15.0%，新能源乘用车上牌88.78万辆，同比+32.8%；批发数据看，2月汽车产销210.3和212.9万辆，产销量环 比-14.1%和-12.2%，同比+39.6%和34.4%；新能源汽车产销完成88.8万辆和89.2万辆，同比+91 ...

Core Viewpoint - The entry of Tesla's Full Self-Driving (FSD) technology into the Chinese market has created a sense of urgency and anxiety among domestic autonomous driving companies, as they fear the potential competitive threat posed by Tesla's advanced AI capabilities [1][5][24]. Summary by Sections Tesla FSD Performance - Tesla's FSD has shown a mixed performance in China, with instances of both impressive driving capabilities and significant errors, highlighting the challenges of adapting to the complex driving environment in China [2][4]. - The underlying AI technology of Tesla is robust, allowing for smooth driving experiences in regular conditions, but it struggles with unique Chinese traffic scenarios due to a lack of localized data training [4][5]. VLA Model Introduction - The VLA model has emerged as a promising solution to the shortcomings of the end-to-end model, integrating visual, linguistic, and action capabilities to enhance vehicle understanding of complex driving situations [8][9]. - VLA's ability to interpret traffic signs and pedestrian intentions positions it as a potential game-changer in the autonomous driving landscape, especially if it can effectively address the unique challenges of Chinese roads [8][12]. Competitive Landscape - Four key players in the domestic market are actively developing VLA technology: Li Auto, Chery, Geely, and Yuanrong Qixing, each with distinct strategies and timelines for implementation [15][16]. - Li Auto's "MindVLA" aims for high accuracy in complex scenarios but faces challenges in managing dual systems, while Chery collaborates with major tech firms to enhance its capabilities [18][19]. - Yuanrong Qixing stands out for its aggressive development and production of VLA technology, positioning itself ahead of competitors in the market [19][21]. Future Outlook - The competition in the autonomous driving sector is shifting from engineering capabilities to the foundational AI model capabilities, with the upcoming deployment of VLA-equipped vehicles expected to provide clarity on the competitive dynamics between Tesla's FSD and domestic technologies [24][25].

香港交易及結算所有限公司及香港聯合交易所有限公司對本公告的內容概不負責，對其準確性 或完整性亦不發表任何聲明，並明確表示概不會就本公告全部或任何部分內容而產生或因倚賴 該等內容而引致的任何損失承擔任何責任。 Li Auto Inc. 理想汽車 （於開曼群島註冊成立以不同投票權控制的有限責任公司） （股份代號：2015） 自願公告 2025年3月交付更新資料 於2025年4月1日，中國新能源汽車市場的領導者理想汽車（「理想汽車」或「本 公司」）（納斯達克：LI；香港交易所：2015）宣佈，2025年3月，理想汽車交付 新車36,674輛，同比增長26.5%。2025年第一季度共計交付92,864輛，同比增長 15.5%。截至2025年3月31日，理想汽車歷史累計交付量為1,226,736輛。 在20萬元以上新能源汽車市場，理想汽車連續12個月獲得中國汽車品牌銷量冠 軍。作為理想汽車快速實現盈利、突破千億元營收的重要基石，理想L系列即 將迎來第100萬輛交付里程碑。理想MEGA Ultra智駕煥新版已開啟預訂，理想 MEGA車型在2025上海車展還將為大家帶來驚喜。本公司於3月宣佈將自研的汽 車操作系統—理想星環 ...

月度产销： 根据乘联会数据初步统计，3月狭义乘用车零售总市场规模约185.0万辆，同比增长9.1%，环比增长 33.7%。2月全国乘用车厂商批发176.7万辆创当月历史新高，同比增长33.8%，环比下降16.0%；1-2月全国乘用车厂商 批发386.5万辆，同比增长12.7%。 周度数据： 根据上险数据，3月10日-3月16日，国内乘用车上牌41.43万辆，同比+21.5%，环比+16.2%；其中新能源乘 用车上牌22.18万辆，同比+38.6%，环比+10.0%。3月累计更新(3.3-3.16)：3月国内乘用车累计上牌77.09万辆，同比 +18.8%；其中新能源乘用车累计上牌42.35万辆，同比+37.2%。 本周行情： 本周（20250317-20250321）CS汽车下跌2.19%，CS乘用车下跌2.84%，CS商用车下跌0.58%，CS汽车零部 件下跌2.26%，CS汽车销售与服务下跌0.16%，CS摩托车及其他下跌0.27%，电动车下跌3.3%，智能车下跌3.08%，同 期的沪深300指数下跌2.32%，上证综合指数下跌1.89%。CS汽车强于沪深300指数0.13pct，弱于上证综合指数0.29 ...

本月行情： 2月CS汽车板块上涨8.66%，其中CS乘用车上涨9.08%，CS商用车上涨2.54%，CS汽车零部件上涨 10.33%，CS汽车销售与服务上涨2.47%，CS摩托车及其它上涨4.61%，同期沪深300指数上涨1.91%，上证综合指数上 涨2.16%，CS汽车板块跑赢沪深300指数6.75pct，跑赢上证综合指数6.5pct；汽车板块自2023年初至今上涨28.71%，沪 深300上涨13.38%，上证综合指数上涨11.63%，CS汽车板块跑赢沪深300指数15.33pct。 成本跟踪： 截至2025年2月28日，浮法平板玻璃、铝锭类、锌锭类价格分别同比去年同期-33.1%/+9.4%/+15.9%，分别 环比上月同期-0.4%/+1.9%/-2.9%。 库存： 2025年2月中国汽车经销商库存预警指数为56.9%，同比下降7.2个百分点，环比下降5.4个百分点，库存预警指 数位于荣枯线之上。 市场关注： 1）智驾进展：奇瑞发布"猎鹰智驾"智能化方案推动智驾平权；吉利发布千里浩瀚智驾系统；理想汽车发 布下一代自动驾驶架构MindVLA；广汽发布智能科技品牌"星灵智行"；2）机器人：长安汽车在机器人 ...

3月18日，理想汽车自动驾驶技术研发负责人贾鹏在NVIDIA GTC 2 0 2 5发表主题演讲， 详细分享了理想汽车自研VLA模型——Mi n dVLA。贾鹏表示："就像iPh o n e 4重新定义 了手机，Mi n dVLA也将重新定义自动驾驶"。能为智能驾驶带来iPh o n e 4时刻的重大技 术，距离用户也并不遥远，在理想汽车2024年第四季度及全年财报业绩会上，李想透露 Mi ndVLA将计划今年和首款纯电SUV车型理想i 8同时发布。 理想全栈自研MindVLA 深度融合空间、语言及行为智能 基于端到端+VLM双系统架构的最佳实践，及对前沿技术的敏锐洞察，理想自研VLA模 型——Mi n dVLA。VLA是机器人大模型的新范式，其将赋予自动驾驶强大的3D空间理 解能力、逻辑推理能力和行为生成能力，让自动驾驶能够感知、思考和适应环境。 Mi ndVLA不是简单地将端到端模型和VLM模型结合在一起，所有模块都是全新设计。 3D空间编码器通过语言模型，和逻辑推理结合在一起后，给出合理的驾驶决策，并输出 一组Ac ti o n To k e n（动作词元），Ac ti on Toke n指的是对周围环 ...