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 小鹏昨天刚刚发布了VLA 2.0，很有意思。 今天柱哥就和大家一起聊下，目前从网上看到的消息有几个关键点总结下： 等后面有更多的信息再详细总结以下，先分享几个网上的信息。 输入有视频、语言文本、指令、Ego，输出Action，另一部分的latent tokens输入到世界仿真器里和Action做交互强化学习。业内的思路整体上都大差不差，还是得看 工程优化做得咋样~ 小鹏的VLA两条路线：V/L→A和V→L→A，V/L→A去掉了语言转译，但仍然以视觉为核心； 首个量产物理世界大模型，最高有效算力达2250TOPS； 世界模型也有参与未来场景预测； 小鹏还是挺舍得在算力上砸钱的，但在一个偶然版本上看到希望... 小鹏VLA的两套方案并行研发，以往的V→L→A和最新的V/L→A。V/L→A更贴合最近特斯拉ICCV分享的内容，L不是作为中间件，而是V的并行输入。 目前开源的几篇算法也有类似的，比如ORION。这样模型可以同步输出感知结果、自车轨迹和对应的思维链。下图是ORION的算法框架： 未来小鹏也将入局robot ...

Core Insights - Zacks Premium offers various tools for investors to enhance their stock market strategies, including daily updates, research reports, and stock screens [1] - The Zacks Style Scores are designed to help investors select stocks with the highest potential to outperform the market in the short term [2] Zacks Style Scores Overview - The Style Scores categorize stocks based on value, growth, and momentum characteristics, assigning ratings from A to F, with A indicating the highest potential for outperformance [3] - Value Score focuses on identifying undervalued stocks using financial ratios like P/E and Price/Cash Flow [3] - Growth Score evaluates stocks based on projected earnings and sales growth, targeting companies with strong financial health [4] - Momentum Score assesses stocks based on price trends and earnings estimate changes, helping investors capitalize on upward or downward movements [5] - VGM Score combines all three Style Scores, providing a comprehensive indicator for investors seeking attractive value, growth, and momentum [6] Zacks Rank and Style Scores Interaction - The Zacks Rank utilizes earnings estimate revisions to guide investors in building successful portfolios, with 1 (Strong Buy) stocks historically yielding an average annual return of +23.93% since 1988 [7] - There are over 800 stocks rated as 1 or 2, making it essential for investors to use Style Scores to narrow down their choices [8] - For optimal returns, investors should focus on stocks with a Zacks Rank of 1 or 2 and Style Scores of A or B [9] - The direction of earnings estimate revisions is crucial; stocks with lower ranks and declining forecasts pose higher risks despite good Style Scores [10] Company Spotlight: Badger Meter - Badger Meter, established in 1905 and based in Milwaukee, WI, specializes in water solutions, including flow measurement and quality monitoring [11] - The company holds a Zacks Rank of 2 (Buy) and has a VGM Score of B, indicating strong growth potential [12] - Badger Meter is projected to achieve year-over-year earnings growth of 13.5% for the current fiscal year, with recent upward revisions in earnings estimates [12] - With a solid Zacks Rank and favorable Style Scores, Badger Meter is recommended for investors seeking growth opportunities [13]

Core Insights - The focus of academia and industry has shifted towards VLA (Vision-Language Action) in autonomous driving, which provides human-like reasoning capabilities for vehicle decision-making [1][4] - Traditional methods in perception and lane detection have matured, leading to decreased attention in these areas, while VLA is now a critical area for development among major autonomous driving companies [4][6] Summary by Sections Introduction to VLA - VLA is categorized into modular VLA, integrated VLA, and reasoning-enhanced VLA, which are essential for improving the reliability and safety of autonomous driving [1][4] Course Overview - A comprehensive course on autonomous driving VLA has been designed, covering foundational principles to practical applications, including cutting-edge algorithms like CoT, MoE, RAG, and reinforcement learning [6][12] Course Structure - The course consists of six chapters, starting with an introduction to VLA algorithms, followed by foundational algorithms, VLM as an interpreter, modular and integrated VLA, reasoning-enhanced VLA, and a final project [12][20] Chapter Highlights - Chapter 1 provides an overview of VLA algorithms and their development history, along with benchmarks and evaluation metrics [13] - Chapter 2 focuses on the foundational knowledge of Vision, Language, and Action modules, including the deployment of large models [14] - Chapter 3 discusses VLM's role as an interpreter in autonomous driving, covering classic and recent algorithms [15] - Chapter 4 delves into modular and integrated VLA, emphasizing the evolution of language models in planning and control [16] - Chapter 5 explores reasoning-enhanced VLA, introducing new modules for decision-making and action generation [17][19] Learning Outcomes - The course aims to deepen understanding of VLA's current advancements, core algorithms, and applications in projects, benefiting participants in internships and job placements [24]

Core Viewpoint - The focus of academia and industry after end-to-end systems is on VLA (Vision-Language-Action), which provides human-like reasoning capabilities for safer and more reliable autonomous driving [1][4]. Summary by Sections Introduction to Autonomous Driving VLA - VLA is categorized into modular VLA, integrated VLA, and reasoning-enhanced VLA, which are essential for advancing autonomous driving technology [1][4]. Technical Maturity and Employment Demand - The demand for autonomous driving VLA solutions is high among major companies, prompting them to invest in self-research and development [4]. Course Overview - A comprehensive learning roadmap for autonomous driving VLA has been designed, covering principles to practical applications [4][6]. Core Content of Autonomous Driving VLA - Key topics include visual perception, large language models, action modeling, model deployment, and dataset creation, with cutting-edge algorithms like CoT, MoE, RAG, and reinforcement learning [6]. Course Collaboration - The course is developed in collaboration with Tsinghua University's research team, featuring detailed explanations of algorithms and practical assignments [6]. Course Structure - The course consists of six chapters, each focusing on different aspects of VLA, including algorithm introduction, foundational algorithms, VLM as an interpreter, modular and integrated VLA, reasoning-enhanced VLA, and a final project [12][20]. Chapter Details - Chapter 1 covers the concept and history of VLA algorithms, including benchmarks and evaluation metrics [13]. - Chapter 2 focuses on foundational algorithms related to Vision, Language, and Action, along with model deployment [14]. - Chapter 3 discusses VLM's role as an interpreter in autonomous driving, highlighting key algorithms [15]. - Chapter 4 delves into modular and integrated VLA, emphasizing the evolution of language models in planning [16]. - Chapter 5 explores reasoning-enhanced VLA, introducing new modules for decision-making and action output [17]. - Chapter 6 involves a hands-on project where participants build and fine-tune their models [20]. Learning Outcomes - The course aims to deepen understanding of VLA's current advancements and core algorithms, equipping participants with practical skills for future research and applications in the autonomous driving sector [22][26]. Course Schedule - The course is set to begin on October 20, with a structured timeline for each chapter's release [23]. Prerequisites - Participants are expected to have a foundational knowledge of autonomous driving, large models, reinforcement learning, and programming skills in Python and PyTorch [26].

Core Viewpoint - The article discusses the development and features of the PLUTO model within the end-to-end autonomous driving domain, emphasizing its unique two-stage architecture and its direct encoding of structured perception outputs for downstream control tasks [1][2]. Summary by Sections Overview of PLUTO - PLUTO is characterized by its three main losses: regression loss, classification loss, and imitation learning loss, which collectively contribute to the model's performance [7]. - Additional auxiliary losses are incorporated to aid model convergence [9]. Course Introduction - The article introduces a new course titled "End-to-End and VLA Autonomous Driving," developed in collaboration with top algorithm experts from domestic leading manufacturers, aimed at addressing the challenges faced by learners in this rapidly evolving field [12][15]. Learning Challenges - The course addresses the difficulties learners face due to the fast-paced development of technology and the fragmented nature of knowledge across various domains, making it hard for beginners to grasp the necessary concepts [13]. Course Features - The course is designed to provide quick entry into the field, build a framework for research capabilities, and combine theory with practical applications [15][16][17]. Course Outline - The course consists of several chapters covering topics such as the history and evolution of end-to-end algorithms, background knowledge on various technologies, and detailed discussions on both one-stage and two-stage end-to-end methods [20][21][22][29]. Practical Application - The course includes practical assignments, such as RLHF fine-tuning, allowing students to apply their theoretical knowledge in real-world scenarios [31]. Instructor Background - The instructor, Jason, has a strong academic and practical background in cutting-edge algorithms related to end-to-end and large models, contributing to the course's credibility [32]. Target Audience and Expected Outcomes - The course is aimed at individuals with a foundational understanding of autonomous driving and related technologies, with the goal of elevating their skills to the level of an end-to-end autonomous driving algorithm engineer within a year [36].

Core Viewpoint - The article discusses the advancements in end-to-end (E2E) autonomous driving technology, particularly focusing on the introduction of the ORION framework, which integrates vision-language models (VLM) for improved decision-making in complex environments [3][30]. Summary by Sections Introduction - Recent progress in E2E autonomous driving technology faces challenges in complex closed-loop interactions due to limited causal reasoning capabilities [3][12]. - VLMs offer new hope for E2E autonomous driving but there remains a significant gap between VLM's semantic reasoning space and the numerical action space required for driving [3][17]. ORION Framework - ORION is proposed as an end-to-end autonomous driving framework that utilizes visual-language instructions for trajectory generation [3][18]. - The framework incorporates QT-Former for aggregating long-term historical context, VLM for scene understanding and reasoning, and a generative model to align reasoning and action spaces [3][16][18]. Performance Evaluation - ORION achieved a driving score of 77.74 and a success rate of 54.62% on the challenging Bench2Drive dataset, outperforming previous state-of-the-art (SOTA) methods by 14.28 points and 19.61% in success rate [5][24]. - The framework demonstrated superior performance in specific driving scenarios such as overtaking (71.11%), emergency braking (78.33%), and traffic sign recognition (69.15%) [26]. Key Contributions - The article highlights several key contributions of ORION: 1. QT-Former enhances the model's understanding of historical scenes by effectively aggregating long-term visual context [20]. 2. VLM enables multi-dimensional analysis of driving scenes, integrating user instructions and historical information for action reasoning [21]. 3. The generative model aligns the reasoning space of VLM with the action space for trajectory prediction, ensuring reasonable driving decisions in complex scenarios [22]. Conclusion - ORION provides a novel solution for E2E autonomous driving by achieving semantic and action space alignment, integrating long-term context aggregation, and jointly optimizing visual understanding and path planning tasks [30].

Core Insights - Theon International Plc is launching the THEON NEXT initiative, focusing on strategic investments and partnerships to develop next-generation soldier systems through targeted collaborations and co-development efforts [1][2] Investment and Partnerships - THEON is making four significant investments and partnerships in the US and Europe, reinforcing its commitment to innovation and transatlantic cooperation in defense technologies [2][3] - A total investment of $15 million in Kopin Corporation includes a $7 million interest-bearing loan and an $8 million capital increase for a 49% stake in Kopin's Scottish subsidiary, aimed at co-developing AR-enabled systems [3][4] - A two-year renewable supply agreement has been signed with eMagin for OLED micro-displays, which are critical for THEON's products [7] - A strategic partnership with ALEREON will integrate Ultra-Wide-Band technology into THEON's A.R.M.E.D. product line, enhancing secure communication capabilities [8] - THEON is investing €5 million in Varjo Technologies Oy, a Finnish company specializing in VR and MR technologies, to support the development of high-tech products for defense applications [9] Technological Focus - The initiative emphasizes three critical technologies: Augmented and Virtual Reality software, micro-displays, and near-range wireless connectivity, which are essential for next-generation soldier systems [5] - THEON's A.R.M.E.D. product line is positioned to leverage these technologies, enhancing operational effectiveness in modern warfare [10] Strategic Goals - Theon aims to maintain its leadership in man-portable electro-optics while fostering US-European industrial cooperation [2][10] - The company plans to expand its operations into Germany and Belgium, establishing a thermal/digital hub in the EU [10] Leadership Statements - The CEO of THEON highlighted the importance of these partnerships in advancing the development of soldier-borne systems and enhancing operational capabilities [10] - The CFO noted that these strategic agreements represent a financially efficient investment approach, with a total investment of €25 million expected to yield quick returns [10]

Core Insights - The article discusses the rapid advancements in end-to-end autonomous driving, particularly focusing on Vision-Language-Action (VLA) models and their applications in the industry [2][3]. Group 1: VLA Model Developments - The introduction of AutoVLA, a new VLA model that integrates reasoning and action generation for end-to-end autonomous driving, shows promising results in semantic reasoning and trajectory planning [3][4]. - ReCogDrive, another VLA model, addresses performance issues in rare and long-tail scenarios by utilizing a three-stage training framework that combines visual language models with diffusion planners [7][9]. - Impromptu VLA introduces a dataset aimed at improving VLA models' performance in unstructured extreme conditions, demonstrating significant performance improvements in established benchmarks [14][24]. Group 2: Experimental Results - AutoVLA achieved competitive performance metrics in various scenarios, with the best-of-N method reaching a PDMS score of 92.12, indicating its effectiveness in planning and execution [5]. - ReCogDrive set a new state-of-the-art PDMS score of 89.6 on the NAVSIM benchmark, showcasing its robustness and safety in driving trajectories [9][10]. - The OpenDriveVLA model demonstrated superior results in open-loop trajectory planning and driving-related question-answering tasks, outperforming previous methods on the nuScenes dataset [28][32]. Group 3: Industry Trends - The article highlights a trend among major automotive manufacturers, such as Li Auto, Xiaomi, and XPeng, to invest heavily in VLA model research and development, indicating a competitive landscape in autonomous driving technology [2][3]. - The integration of large language models (LLMs) with VLA frameworks is becoming a focal point for enhancing decision-making capabilities in autonomous vehicles, as seen in models like ORION and VLM-RL [33][39].