Core Insights - The financing scale for domestic L4 autonomous driving is expected to exceed 30 billion RMB by 2025, marking a historical high and a growth of approximately 257% compared to 2023, which was around 8.4 billion RMB [2] - Logistics delivery and Robotaxi are identified as the two main tracks attracting significant funding, with leading companies and established scenarios receiving financial support [2] - The industry is entering a technical deep-water zone, necessitating more dedicated individuals to address the challenges and pain points [2] Key Directions - The main focus areas include but are not limited to: autonomous driving product management, 4D annotation/data loop, world models, VLA, autonomous driving large models, reinforcement learning, and end-to-end solutions [4] Recent Financing Activities - Zhaofu Intelligent raised 3 billion RMB in June, with investments from Hello Chuxing, Ant Group, and CATL - New Stone Unmanned Vehicles secured 600 million USD in October led by Leishi Capital from the UAE - Pony.ai went public on the Hong Kong Stock Exchange in November, raising approximately 7.7 billion HKD - WeRide also went public on the Hong Kong Stock Exchange in November, raising about 2.4 billion HKD - Didi Autonomous Driving completed a D round financing of 2 billion RMB in October to enhance AI R&D and promote L4 autonomous driving applications - Zhuoyue Technology received a strategic investment of 3.6 billion RMB from China FAW in November [7]

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 DGGT详解 DGGT 的核心思想是 ：一次前向就预测出"完整的4D场景状态"，并把相机位姿从前提变成结果。这使得系统无需外参标定即可从稀疏、未标定图像里恢复动态场 景，而且能自然跨数据集部署。图1展示了DGGT 的整体能力与速度-精度位置：在 0.4 秒 量级完成重建的同时，DGGT 在重建质量上超越一系列前向与优化方法，并 将 相机姿态、深度、动态分割、3D Gaussian、追踪 等输出一并给出，便于后续 实例级场景编辑 。 >>自动驾驶前沿信息获取 → 自动驾驶之心知识星球 论文作者 | Xiaoxue Chen等 编辑 | 自动驾驶之心 清华大学与小米汽车联合推出 DGGT（Driving Gaussian Grounded Transformer）：一个pose-free、feed-forward的4D动态驾驶场景重建框架。 DGGT 只需未标定的稀疏图像，单次前向即可同时输出相机位姿、深度、动态实例与基于 3D Gaussian 的可编辑场景表示。模型在 Waymo 上训练，却能在 nuScen ...

Core Viewpoint - The article introduces the "Black Warrior 001," a cost-effective and user-friendly autonomous driving educational vehicle designed for research and teaching purposes, priced at 36,999 yuan, which includes various advanced features and training courses [2][4]. Group 1: Product Overview - The Black Warrior 001 is a lightweight solution that supports multiple functionalities such as perception, localization, fusion, navigation, and planning, built on an Ackermann chassis [4]. - It is suitable for various educational levels, including undergraduate learning, graduate research, and training in vocational schools [4]. Group 2: Performance Demonstration - The vehicle has been tested in various environments, including indoor, outdoor, and parking garage scenarios, showcasing its capabilities in perception, localization, fusion, navigation, and planning [6]. Group 3: Hardware Specifications - Key sensors include: - 3D LiDAR: Mid 360 - 2D LiDAR: Lidar from LidarGod - Depth Camera: Orbbec with IMU - Main Control Chip: Nvidia Orin NX 16G - Display: 1080p [22][23]. - The vehicle weighs 30 kg, has a battery power of 50W, operates at 24V, and has a maximum speed of 2 m/s [25][26]. Group 4: Software and Functionality - The software framework includes ROS, C++, and Python, with one-click startup and a provided development environment [28]. - Functional capabilities include various SLAM techniques, vehicle navigation, and obstacle avoidance [29]. Group 5: After-Sales and Support - The company offers one year of after-sales support for non-human damage, with free repairs for damages caused by user errors during the warranty period [52].

作者 | 我要吃鸡腿 编辑 | 大模型之心Tech 原文链接： https://zhuanlan.zhihu.com/p/1963658684765833212 本文只做学术分享，已获转载授权 ，欢迎添加小助理微信AIDriver004做进一步咨询 点击下方 卡片 ，关注" 大模型之心Tech "公众号 戳我-> 领取大模型巨卷干货 引言：当 AI 睁开双眼，我们看到了一个怎样的未来？ 曾几何时，我们对人工智能的印象还停留在那个聪慧但略显"盲目"的"数字大脑"上——它能写诗、能编程、能回答深奥的哲学问题，但这一切都局限 于冰冷的文本世界。然而，就在最近两年，一场深刻的变革正在悄然发生。 您或许已经惊叹于 GPT-5 那般流畅自如的实时图片对话，它能"看到"您房间的布局并给出整理建议；又或者，您可能对 Qwen3-VL 直接"注视"着手 机屏幕、精准地点击按钮、操作应用程序的能力感到不可思议。AI 不再仅仅是一个"只会读书"的语言模型，它正在进化成一个能听、会看、可交互 的"智能体"，真正地睁开了双眼，开始感知和理解我们所处的这个五彩斑斓的物理世界。 这场从"符号"到"感知"的飞跃，背后究竟隐藏着怎样的技术密码 ...

Core Insights - The article discusses the evolving recruitment landscape in the autonomous driving industry, highlighting a shift in demand from perception roles to end-to-end, VLA, and world model positions [2] - A new course titled "End-to-End Practical Class for Mass Production" has been designed to address the skills gap in the industry, focusing on practical applications and mass production experiences [2][4] Course Overview - The course aims to cover core algorithms such as one-stage and two-stage end-to-end methods, navigation information applications, reinforcement learning, and trajectory optimization [2] - It is structured into eight chapters, each focusing on different aspects of end-to-end autonomous driving systems, including task overview, algorithm frameworks, navigation applications, and production experiences [5][7][8][9][10][11][12][13][14] Target Audience - The course is designed for advanced learners with a background in autonomous driving perception, reinforcement learning, and programming languages like Python and PyTorch [15][16] - It emphasizes practical skills and aims to prepare participants for real-world applications in the autonomous driving sector [2][15] Course Schedule - The course will commence on November 30, with a duration of approximately three months, featuring offline video lectures and online Q&A sessions [15][17]

点击下方 卡片 ，关注 "红岸" 公众号 >> 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。 前两天分享了清华具身研究院和上交的具身专业开设，除了这两所，还有另外6所双一流高校正在申请增 设"具身智能本科专业"。以下为教育部公示的名单。 | 学校名称 | 专业名称 | 学位授予门类 | 申报类型 | 申请表 | | --- | --- | --- | --- | --- | | 北京航空航天大学 | 具身智能 | 工学 | 尚未列入目录的新专业 | 下载 | | 北京理工大学 | 具身智能 | 工学 | 尚未列入目录的新专业 | 下载 | | 北京邮电大学 | 具身智能 | 工学 | 尚未列入目录的新专业 | 下载 | | 东北大学 | 具身智能 | 工学 | 尚未列入目录的新专业 | 下載 | | 上海交通大学 | 具身智能 | 工学 | 尚未列入目录的新专业 | 下载 | | 浙江大学 | 具身智能 | 工学 | 尚未列入目录的新专业 | 下载 | | 西安交通大学 | 具身智能 | 工学 | 尚未列 ...

Core Viewpoint - The article discusses a novel multi-scale bilateral grid framework for 3D scene reconstruction in autonomous driving, addressing challenges such as lighting variations and dynamic objects, leading to improved geometric accuracy and visual quality [5][10][39]. Group 1: Methodology - The proposed framework combines the strengths of appearance codes and bilateral grids to achieve efficient and accurate scene reconstruction [11][13]. - The architecture employs Gaussian splatting to model complex driving scenes, decomposing them into a mixed scene graph that includes independent modeling of static and dynamic elements [14]. - The framework consists of three levels: coarse, intermediate, and fine, each addressing different aspects of lighting and detail adjustments [15]. Group 2: Experimental Results - Extensive experiments on datasets like Waymo, NuScenes, Argoverse, and PandaSet demonstrate that the proposed method significantly outperforms existing models in geometric accuracy and appearance consistency [19][39]. - In the Waymo dataset, the chamfer distance (CD) improved from 1.378 (OmniRe) to 0.989, a 28.2% enhancement [21]. - The method achieved a PSNR of 27.69 and an SSIM of 0.847 on the NuScenes dataset, surpassing OmniRe's scores of 26.37 and 0.837 respectively [23]. Group 3: Robustness and Versatility - The framework shows enhanced performance in extreme scenarios such as night scenes and varying lighting conditions, proving its robustness [27][39]. - The method can be integrated as a plug-and-play enhancement module into existing models like ChatSim and StreetGS, resulting in significant improvements in reconstruction quality [25][26]. Group 4: Future Directions - The research team plans to further optimize the framework for larger and more complex scenes and explore more efficient computational methods for practical applications in autonomous driving [40].

作者 | 我要吃鸡腿 编辑 | 自动驾驶之心 原文链接： https://zhuanlan.zhihu.com/p/1965839552158623077 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 在自动驾驶这个飞速迭代的领域，技术范式的更迭快得令人目不暇接。前年，行业言必称BEV（鸟瞰图视 角）；去年，"端到端"（End-to-End）又成了新的技术高地。然而，每一种范式在解决旧问题的同时，似乎都 在催生新的挑战。 传统的"端到端"自动驾驶，即VA（Vision-Action，视觉-行动）模型，就暴露出一个深刻的矛盾：它就像一个 车技高超但沉默寡言的"老司机"。它能凭借海量数据训练出的"直觉"，在复杂的路况中做出令人惊叹的丝滑操 作。但当您坐在副驾，心脏漏跳一拍后问它："刚才为什么突然减速？"——它答不上来。 这就是"黑箱"问题：系统能"做对"，但我们不知道它"为何做对"。这种无法解释、无法沟通的特性，带来了巨 大的信任危机。 自动驾驶的三大范式演进。(a) ...

Core Insights - The article emphasizes the importance of a comprehensive community for autonomous driving, aiming to provide a platform for knowledge sharing and networking among industry professionals and academic experts [8][25][29]. Community Development - The "Autonomous Driving Heart Knowledge Planet" has been established to facilitate discussions on technology, trends, and changes in the autonomous driving sector, with over 4,000 members and a goal to reach nearly 10,000 in the next two years [8][11]. - The community offers a variety of resources, including videos, articles, learning paths, and job exchange opportunities, making it a valuable hub for both beginners and advanced learners [8][11][12]. Technical Resources - The community has compiled over 40 technical routes covering various aspects of autonomous driving, such as end-to-end learning, multi-modal models, and sensor fusion, which significantly reduces the time needed for research [11][25]. - Members can access detailed information on the latest advancements in autonomous driving technologies, including world models, VLA (Vision Language Models), and 3D target detection [25][49][51]. Job Opportunities - The community provides job referral mechanisms with various autonomous driving companies, ensuring members can connect with potential employers quickly [17][25]. - Regular updates on job openings and industry trends are shared, helping members stay informed about career opportunities in the autonomous driving field [30][100]. Educational Content - The community offers a structured learning path for newcomers, including foundational courses in mathematics, computer vision, and deep learning, tailored for those with no prior experience [19][25]. - Members can participate in live discussions and Q&A sessions with industry leaders, enhancing their understanding of current challenges and innovations in autonomous driving [12][92].

Core Insights - The article discusses the development and application of 3D Gaussian Splatting (3DGS) technology, emphasizing its significance in the field of autonomous driving and 3D reconstruction [3][9]. Group 1: Course Overview - The course titled "3DGS Theory and Algorithm Practical Tutorial" aims to provide a comprehensive learning roadmap for 3DGS, covering both theoretical and practical aspects [3][6]. - The course is designed for individuals interested in entering the 3DGS field, focusing on essential concepts such as point cloud processing and deep learning [3][6]. Group 2: Course Structure - Chapter 1 introduces foundational knowledge in computer graphics, including implicit and explicit representations of 3D space, rendering pipelines, and tools like SuperSplat and COLMAP [6][7]. - Chapter 2 delves into the principles and algorithms of 3DGS, covering dynamic reconstruction and surface reconstruction, with practical applications using the NVIDIA open-source 3DGRUT framework [7][8]. - Chapter 3 focuses on the application of 3DGS in autonomous driving simulations, highlighting key works and tools like DriveStudio for practical learning [8][9]. - Chapter 4 discusses important research directions in 3DGS, including COLMAP extensions and depth estimation, along with insights on their industrial and academic relevance [9][10]. - Chapter 5 covers Feed-Forward 3DGS, detailing its development and algorithmic principles, including recent works like AnySplat and WorldSplat [10]. - Chapter 6 provides a platform for Q&A and discussions on industry demands and challenges related to 3DGS [11]. Group 3: Target Audience and Requirements - The course is aimed at individuals with a background in computer graphics, visual reconstruction, and familiarity with technologies like NeRF and 3DGS [15]. - Participants are expected to have a basic understanding of probability theory, linear algebra, and proficiency in Python and PyTorch [15].