Core Insights - The article highlights the growth of the autonomous driving industry, indicating that it has evolved from a small workshop into a platform with significant technological depth and breadth, with increasing business lines and demand in the market [1] Group 1: Team Overview - The team has spent over two years developing four key IPs: embodied intelligence, autonomous driving, 3D vision, and large model technology, with a total online following of nearly 360,000 across various platforms [1] - The team operates on multiple platforms including WeChat, video accounts, Zhihu, and Bilibili, indicating a broad outreach strategy [1] Group 2: Job Opportunities - The company is currently hiring for full-time and part-time positions in operations and sales, reflecting its expansion and need for additional personnel [2] - The responsibilities for the operations role include managing course progress, enhancing platform engagement, and content creation related to the autonomous driving and AI sectors [4] - The sales role involves creating promotional content for online and hardware products and liaising with hardware manufacturers and academic institutions [5][6] Group 3: Growth and Learning Opportunities - The company offers significant growth opportunities, allowing employees to learn from top operational teams and gain insights into sales strategies [7] - Employees will have exposure to cutting-edge content in fields such as autonomous driving and AI, broadening their technical understanding and industry perspective [8] - There are also opportunities for further academic pursuits, such as pursuing graduate or doctoral studies, which can enhance personal development [9]

Core Insights - The article presents a new autonomous driving decision-making algorithm framework called Flow Planner, which improves upon the existing Diffusion Planner by effectively modeling advanced interactive behaviors in high-density traffic scenarios [1][4][22]. Group 1: Background and Challenges - One of the core challenges in autonomous driving planning is achieving safe and reliable human-like decision-making in dense and diverse traffic environments [3]. - Traditional rule-based methods lack generalization capabilities in dynamic traffic games, while learning-based methods struggle with limited high-quality training data and the need for effective game behavior modeling [6][8]. Group 2: Innovations of Flow Planner - Flow Planner introduces three key innovations: fine-grained trajectory tokenization, interaction-enhanced spatiotemporal fusion, and classifier-free guidance for trajectory generation [4][23]. - Fine-grained trajectory tokenization allows for better representation of trajectories by dividing them into overlapping segments, improving coherence and diversity in planning [8]. - The interaction-enhanced spatiotemporal fusion mechanism enables the model to effectively capture spatial interactions and temporal consistency among various traffic participants [9][13]. - Classifier-free guidance allows for flexible adjustment of model sampling distributions during inference, enhancing the generation of driving behaviors and strategies [10]. Group 3: Experimental Results - Flow Planner achieved state-of-the-art (SOTA) performance on the nuPlan benchmark, surpassing 90 points on the Val14 benchmark without relying on any rule-based prior or post-processing modules [11][14]. - In the newly proposed interPlan benchmark, Flow Planner significantly outperformed other baseline methods, demonstrating superior response strategies in high-density traffic and pedestrian crossing scenarios [15][20]. Group 4: Conclusion - The Flow Planner framework significantly enhances decision-making performance in complex traffic interactions through its innovative modeling approaches, showcasing strong potential for adaptability across various scenarios [22][23].

Core Viewpoint - The autonomous driving industry is entering a period of consolidation and technological convergence, leading to increased competition and challenges for individuals in the field. The focus is shifting towards comprehensive talent with diverse skill sets, as the market becomes more competitive and the need for innovation grows [2][4][14]. Group 1: Industry Trends - The autonomous driving sector is experiencing a "cooling period" where many professionals are considering transitioning to other fields due to the intense competition and lack of internship opportunities [2]. - The ongoing debate between VLA (Vision-Language Alignment) and WA (Wide-Angle) approaches signifies a larger industry transformation, highlighting the need for adaptability among professionals [2][4]. - The community aims to create a knowledge-sharing platform to help individuals navigate the complexities of the autonomous driving landscape, fostering collaboration and innovation [4][14]. Group 2: Community and Resources - The "Autonomous Driving Heart Knowledge Planet" has been established as a comprehensive community for learning and sharing knowledge, currently hosting over 4,000 members with a goal of reaching 10,000 in two years [4][14]. - The community provides a variety of resources, including video tutorials, learning pathways, and Q&A sessions, to assist both beginners and advanced learners in the field [6][10]. - Members have access to a wealth of information, including over 40 technical routes and numerous industry insights, which can significantly reduce the time needed for research and learning [6][15]. Group 3: Learning and Development - The community offers structured learning paths for newcomers, covering essential topics such as multi-sensor fusion, end-to-end autonomous driving, and various algorithms [15][36]. - Regular discussions with industry experts are held to explore trends, challenges, and practical applications in autonomous driving, providing members with valuable insights [7][19]. - The platform also facilitates job opportunities by connecting members with potential employers and providing resume submission services [10][19].

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 Insights - The article discusses the evolution of intelligent driving maps, highlighting the transition from high-precision maps to lightweight maps in response to the growing demand for advanced driver-assistance systems (ADAS) [4][5][12]. Group 1: Evolution of Intelligent Driving Maps - The development of intelligent driving maps has gone through three stages: the sweet period of high-precision maps (2018-2021), the aggressive phase of "no map driving" post-2021, and the current rational phase focusing on lightweight maps as of 2024 [7][9][12]. - The shift to lightweight maps is driven by the need for safety, continuity, and comfort in driving experiences, as traditional high-precision maps are not feasible for widespread use across various terrains [10][12][15]. Group 2: Market Dynamics and Competition - Tencent has emerged as a leader in the intelligent driving map market, holding a 49.01% market share in the urban NOA intelligent driving map segment for new energy passenger vehicles, while Gaode follows closely with 47.9% [5][18]. - The market for intelligent driving maps is expected to grow significantly, with projections estimating it will reach 5.4 billion yuan by 2025 and 11.7 billion yuan by 2030 [27]. Group 3: Technological Innovations - Tencent's transition to lightweight high-precision maps (HD Air) began in 2022, allowing for faster updates and lower costs, which has positioned it favorably against traditional map providers [19][20]. - The introduction of cloud services for map data delivery enhances the flexibility and responsiveness of map updates, allowing for real-time adjustments based on driving conditions [22][24]. Group 4: Future Trends - The integration of AI large models is expected to further transform the landscape of intelligent driving maps, moving beyond traditional databases to models that incorporate geographical and environmental data [29][31]. - The competition in the intelligent driving map sector is ongoing, with companies needing to adapt to new paradigms to maintain or gain market share [26][31].

作者 | 林夕@知乎 来源 | 青稞AI 原文链接： https://zhuanlan.zhihu.com/p/690801254 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 >>自动驾驶前沿信息获取 → 自动驾驶之心知识星球 本文只做学术分享，如有侵权，联系删文 面试情况 投过的公司 ：淘天，字节，蚂蚁，商汤，美团，夸克，腾讯，minimax，零一万物，阿里控股，潞晨科技，阿里巴巴国际，网易实验室，Momenta。 Offer ：淘天，字节AML，商汤，蚂蚁，美团，夸克，腾讯混元，天翼云。 以下是面经分享 淘天【offer】 部门：未来生活实验室 介绍: 淘天集团的大模型研究将主要围绕两个场景展开：一是搜广推，二是逛逛的内容化。团队组建工作由淘天集团CEO戴珊、淘天集团CTO若海、阿里妈妈 CTO郑波等人共同牵头。 面经 一面： HR面： 面试体验 面试体验很好。HR也没有那么咄咄逼人。阿里味不是特别明显。最终权衡也选择来了淘天，有兴趣来我们这边的欢迎投递简历，有卡（****张）。 字节AML【offer】 部门：AML火山方舟大模型 介绍： 淘天集 ...

以下文章来源于具身智能之心 ，作者Shuanghao Bai等 具身智能之心 . 与世界交互，更进一步 点击下方 卡片 ，关注" 具身智能 之心 "公众号 作者丨 Shuanghao Bai等 编辑丨具身智能之心 本文只做学术分享，如有侵权，联系删文 >> 点击进入→ 具身智能之心 技术交流群 更多干货，欢迎加入国内首个具身智能全栈学习社区 ： 具身智能之心知识星球 (戳我) ， 这里包含所有你想要的。 本文作者来自：西安交通大学、香港科技大学（广州）、中国科学院自动化所、西湖大学、浙江大学、悉尼大学、北京智源人工智能研究院、北京大学。 当下，随着大语言模型（LLMs）与多模态模型（MLLMs）的突破，人工智能正以前所未有的速度从"会说"迈向"会做"。 具身智能（Embodied Intelligence）成为连接认知与行动的关键前沿：只有让智能体能够在真实环境中感知、推理并执行操作，才能迈向真正的通用智能 （AGI）。而在这一过程中，机器人操作（Robot Manipulation）扮演着核心角色——它让机器人不仅"理解世界"，更能"改变世界"。 从早期的规则控制与运动规划，到如今融合强化学习、模仿学习与大 ...

Core Insights - The article discusses the advancements in end-to-end autonomous driving, specifically focusing on the SeerDrive model, which aims to improve trajectory planning by incorporating bidirectional modeling of trajectory planning and scene evolution [1][3][4]. Group 1: SeerDrive Overview - SeerDrive introduces a bidirectional modeling paradigm that captures scene dynamics while allowing planning results to optimize scene predictions, creating a closed-loop iteration [3][4]. - The overall pipeline of SeerDrive consists of four main modules: feature encoding, future BEV world modeling, future perception planning, and iterative optimization [4]. Group 2: Challenges in Current Systems - Current one-shot paradigms in autonomous driving overlook dynamic scene evolution, leading to inaccurate planning in complex interactions [5]. - Existing systems fail to model the impact of vehicle behavior on the surrounding environment, which is crucial for accurate trajectory planning [5]. Group 3: Technical Components - Feature encoding transforms multimodal sensor inputs and vehicle states into structured features, laying the groundwork for subsequent modeling [8][9]. - Future BEV world modeling predicts scene dynamics by generating future BEV features, balancing efficiency and structured representation [10][13]. Group 4: Planning and Optimization - SeerDrive employs a decoupled strategy for planning, allowing current and future scenes to guide planning separately, thus avoiding representation entanglement [15]. - The iterative optimization process enhances the bidirectional dependency between trajectory planning and scene evolution, leading to improved performance [17]. Group 5: Experimental Results - SeerDrive achieved a PDMS score of 88.9 on the NAVSIM test set, outperforming several state-of-the-art methods [23]. - In the nuScenes validation set, SeerDrive demonstrated an average L2 displacement error of 0.43m and a collision rate of 0.06%, significantly better than competing methods [24]. Group 6: Component Effectiveness - The removal of future perception planning or iterative optimization resulted in a decrease in PDMS scores, indicating the importance of these components for performance enhancement [26]. - The design choices, such as the decoupled strategy and the use of anchored endpoints for future ego feature initialization, proved to be critical for achieving optimal results [30]. Group 7: Limitations and Future Directions - The BEV world model does not leverage the generalization capabilities of foundational models, which could enhance performance in complex scenarios [41]. - Future research may explore the integration of foundational models with planning to improve generalization while maintaining efficiency [41].

Core Insights - The article discusses the significant technological advancements in autonomous driving over the past decade, highlighting key innovations such as Visual Transformers, BEV perception, multi-sensor fusion, end-to-end autonomous driving, large models, VLA, and world models [3][4]. Group 1: Technological Milestones - The past ten years have seen remarkable technological developments in autonomous driving, with various solutions emerging through the collision and fusion of different technologies [3]. - A roundtable discussion is set to reflect on the technological milestones in the industry, focusing on the debate between world models and VLA [4][13]. Group 2: Industry Perspectives - The roundtable will feature insights from top industry leaders, discussing the evolution of autonomous driving technology and providing career advice for newcomers in the field [4][5]. - The discussion will also cover the perspectives of academia and industry regarding L3 autonomous driving, emphasizing the convergence of research directions and the practical implementation in engineering [13]. Group 3: Future Directions - The article raises questions about the future direction of autonomous driving technology, particularly the role of end-to-end systems as a foundational element of intelligent driving technology [13]. - It highlights the ongoing competition between academic research and engineering practices in the field, suggesting a need for new entrants to adapt and innovate [13].

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