虽然从上半年开始，我们一直在筹办相关事宜。但众人拾柴火焰高，我们需要更多优秀的伙伴加入我们。 现面向全球的自动驾驶领域从业者发出邀请函，自动驾驶之心期望能够和您在技术服务、培训、课程开发与科 研辅导等多个领域展开合作。 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 最近收到越来越多业内小伙伴和公司的诉求，希望自动驾驶之心能够在企业培训和求职辅导等方向上赋能。 企业培训的需求是多样的，从技术进展的梳理、到发布会方案的解读，以及行业发展的总结。 寻求辅导的同学往往受困于简历不够亮眼，急需一些项目补充和经验传授。 我们将提供高额的酬金与丰富的行业资源。 主要方向 岗位说明 主要面向自动驾驶培训合作（B端主要面向企业和高校、研究院所培训，C端面向较多学生、求职类人群）、 课程开发和原创文章创作。 联系我们 感兴趣的可以添加微信wenyirumo做进一步咨询。 包括但不限于：自动驾驶产品经理、4D标注/数据闭环、世界模型、VLA、自动驾驶大模型、强化学习、端到 端等多个方向。 ...

Core Viewpoint - The article discusses the challenges and pressures faced by a leading automotive company's self-driving research team as they approach critical deadlines for developing advanced autonomous driving technologies, highlighting the competitive landscape and the importance of effective management in achieving technological advancements [6][8][14]. Group 1: Development Goals and Challenges - The self-driving research team of a leading automotive company has set ambitious internal goals to develop a no-map urban Navigation on Autopilot (NOA) by September 30 and an end-to-end system by December 30 [6]. - The company is currently lagging behind new entrants and leading autonomous driving firms by at least a year in terms of research and development progress [8]. - The pressure is high for the smart driving leaders, as failure to meet these deadlines could lead to accountability issues and organizational turmoil [7][8]. Group 2: Investment and Talent Acquisition - The company has significantly increased its investment in autonomous driving technology, surpassing that of some new entrants, and is willing to offer competitive salaries to attract top talent [9]. - Unlike some new entrants that offer compensation packages tied to stock performance, this leading company provides more cash to avoid fluctuations in employee compensation due to stock price volatility [9]. Group 3: Technical and Management Issues - Despite substantial investments, the company faces challenges in the end-to-end development process, particularly in data management, which is crucial for training models effectively [10]. - Traditional automotive companies often struggle with a lack of algorithmic expertise among their leadership, which affects their ability to manage and innovate in autonomous driving technology [13]. - The management approach in traditional firms tends to focus on coding output rather than the underlying algorithmic thought processes, which contributes to lower technical output compared to new entrants [14]. Group 4: Future Outlook and User Experience - The company plans to widely implement high-level urban NOA in numerous models next year, contingent on the success of its self-developed end-to-end system [15]. - The upcoming year is expected to be pivotal for end-to-end systems, as both new entrants and leading firms are achieving performance levels that meet consumer expectations [15]. - The emphasis will shift towards ensuring that the technology not only functions but also provides a satisfactory user experience, as performance differences among various end-to-end systems can significantly impact consumer perception [16].

Core Viewpoint - The intelligent driving industry is at a critical juncture with the emergence of VLA (Vision-Language-Action) technology, leading to a division among key players regarding its potential and implementation [1][2][3]. Group 1: VLA Technology and Its Implications - VLA is seen as a potential solution to the limitations of end-to-end systems in intelligent driving, which can only address about 90% of the challenges [6][10]. - The introduction of language as a bridge in the VLA model aims to enhance the system's understanding and decision-making capabilities, allowing for more complex and nuanced driving actions [12][14][18]. - VLA is believed to improve three key areas: understanding dynamic traffic signals, enabling natural voice interactions, and enhancing risk prediction capabilities [19][20][21]. Group 2: Challenges and Criticisms of VLA - Despite the potential advantages, VLA faces significant challenges, including the need for substantial financial investment and the technical difficulties of aligning multimodal data [31][32]. - Critics argue that VLA may not be necessary for achieving higher levels of autonomous driving, with some suggesting it is more of a supplementary enhancement rather than a fundamental solution [35][36]. - The current limitations of existing intelligent driving chips hinder the effective deployment of VLA models, raising concerns about their practical application in real-world scenarios [31][32]. Group 3: Industry Perspectives and Strategies - Companies like Li Auto, Yuanrong, and Xiaopeng are betting on VLA, emphasizing high investment and computational intensity to pursue its development [41][42]. - In contrast, players like Huawei and Horizon are focusing on structural solutions and world models, arguing that these approaches may offer more reliable paths to achieving advanced autonomous driving [43][46]. - The ongoing debate over VLA reflects broader strategic choices within the industry, with companies prioritizing different technological pathways based on their resources and market positioning [47].

Core Insights - The article focuses on advancements in embodied intelligence and robotic manipulation, highlighting various research projects and methodologies aimed at improving robotic capabilities in real-world applications [2][3][4]. Group 1: 2025 Research Initiatives - Numerous projects are outlined for 2025, including "Steering Your Diffusion Policy with Latent Space Reinforcement Learning" and "Chain-of-Action: Trajectory Autoregressive Modeling for Robotic Manipulation," which aim to enhance robotic manipulation through advanced learning techniques [2][3]. - The "BEHAVIOR Robot Suite" is designed to streamline real-world whole-body manipulation for everyday household activities, indicating a focus on practical applications of robotics [2]. - "You Only Teach Once: Learn One-Shot Bimanual Robotic Manipulation from Video Demonstrations" emphasizes the potential for efficient learning methods in robotic training [2][3]. Group 2: Methodologies and Techniques - The article discusses various methodologies such as "Adaptive 3D Scene Representation for Domain Transfer in Imitation Learning" and "Learning the RoPEs: Better 2D and 3D Position Encodings with STRING," which aim to improve the adaptability and efficiency of robotic systems [2][3][4]. - "RoboGrasp: A Universal Grasping Policy for Robust Robotic Control" highlights the development of a versatile grasping policy that can be applied across different robotic platforms [2][3]. - "Learning Dexterous In-Hand Manipulation with Multifingered Hands via Visuomotor Diffusion" showcases advancements in fine motor skills for robots, crucial for complex tasks [4]. Group 3: Future Directions - The research emphasizes the importance of integrating visual and tactile feedback in robotic systems, as seen in projects like "Adaptive Visuo-Tactile Fusion with Predictive Force Attention for Dexterous Manipulation" [7]. - "Zero-Shot Visual Generalization in Robot Manipulation" indicates a trend towards developing robots that can generalize learned skills to new, unseen scenarios without additional training [7]. - The focus on "Human-to-Robot Data Augmentation for Robot Pre-training from Videos" suggests a shift towards leveraging human demonstrations to enhance robotic learning processes [7].