Core Viewpoint - The article announces the launch of the "Black Warrior Series 001," a lightweight autonomous driving solution aimed at research and education, with a promotional price of 34,999 yuan and a deposit scheme for early orders [1]. Group 1: Product Overview - The Black Warrior 001 is developed by the Autonomous Driving Heart team, featuring a comprehensive solution that supports perception, localization, fusion, navigation, and planning, built on an Ackermann chassis [2]. - The product is designed for various educational and research applications, including undergraduate learning, graduate research, and as teaching tools in laboratories and vocational schools [5]. Group 2: Performance Demonstration - The product has been tested in multiple environments, including indoor, outdoor, and underground scenarios, showcasing its capabilities in perception, localization, fusion, navigation, and planning [3]. Group 3: Hardware Specifications - Key sensors include: - 3D LiDAR: Mid 360 - 2D LiDAR: Lidar Intelligent - Depth Camera: Orbbec, with built-in IMU - Main Control Chip: Nvidia Orin NX 16G - Display: 1080p [19] - The vehicle's weight is 30 kg, with a battery power of 50W and a voltage supply of 24V, providing a runtime of over 4 hours [21]. Group 4: Functional Capabilities - The system supports various functionalities such as 2D and 3D SLAM, point cloud processing, vehicle navigation, and obstacle avoidance [24]. Group 5: Software Framework - The software framework includes ROS, C++, and Python, allowing for one-click startup and providing a development environment for users [23]. Group 6: After-Sales and Maintenance - The company offers one year of after-sales support (excluding human damage), with free repairs for damages caused by operational errors or code modifications during the warranty period [46].

作者 | Fanzhi Zeng 来源 | 深蓝AI 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15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 摘要 基于规则 的决策系统 通过定义明确的规则来指导车辆行为， 具备很好的透明性和可解释性。但也存在 着 高度依赖于专家知识，开发成本高昂，对复杂动态交通环境的适应性有限 等问题。 针对上述提到的相关问题，并且考虑到目前大语言模型 展现出丰富的世界知识和强大的推理能力 。本 文提出了一种新颖的基于规则决策的LLM驱动的自动驾驶框架 ADRD 。在自动驾驶仿真平台highway- env上的实验结果表明， ADRD在多种典型驾驶场景中表现出强大的泛化能力和鲁棒性。与传统的知识 驱动方法和数据驱动的强化学习方法相比，ADRD在决策性能、响应效率和可解释性方面取得了显著提 升。 论文标题： ADRD: LLM-Driven Autonomous Driving Based on Rule-based Decision Systems 论文作者： Fanzhi Zeng, S ...

Core Viewpoint - The article discusses the development and implementation of the Street Gaussians algorithm for dynamic scene representation in autonomous driving, highlighting its efficiency in training and rendering compared to previous methods [2][3]. Group 1: Background and Challenges - Previous methods faced challenges such as slow training and rendering speeds, as well as inaccuracies in vehicle pose tracking [3]. - Street Gaussians aims to generate realistic images for view synthesis in dynamic urban street scenes by modeling them as a combination of foreground moving vehicles and static backgrounds [3][4]. Group 2: Technical Implementation - The background model is represented as a set of points in world coordinates, each assigned a 3D Gaussian to represent geometry and color, with parameters optimized to avoid invalid values [8]. - The object model for moving vehicles includes a set of optimizable tracking poses and point clouds, with similar Gaussian attributes to the background model but defined in local coordinates [11]. - A 4D spherical harmonic model is introduced to encode temporal information into the appearance of moving vehicles without high storage costs [12]. Group 3: Initialization and Data Handling - Street Gaussians utilizes aggregated LiDAR point clouds for initialization, addressing the limitations of traditional SfM point clouds in urban environments [17]. - For objects with fewer than 2,000 LiDAR points, random sampling is employed to ensure sufficient data for model initialization [17]. Group 4: Course and Learning Opportunities - The article promotes a specialized course on 3D Gaussian Splatting (3DGS), covering various subfields and practical applications in autonomous driving, aimed at enhancing understanding and implementation skills [26][35].

Core Viewpoint - Youjia Innovation (佑驾创新) is actively expanding its smart driving and smart cockpit solutions, securing multiple projects in the first half of the year, and is planning a share placement to raise approximately HKD 158 million for further development and commercialization of its technologies [2][3][4]. Group 1: Financing and Use of Proceeds - Youjia Innovation announced a share placement at HKD 23.26 per share, representing a 14.80% discount from the previous closing price of HKD 27.30, aiming to raise about HKD 158 million [2]. - The net proceeds from the placement are expected to be approximately HKD 155 million, with allocations of 40% for enhancing smart driving solutions, 30% for L4 autonomous driving technology upgrades, 20% for exploring strategic partnerships and acquisitions, and 10% for operational funds [2][4]. Group 2: Business Growth and Market Demand - The company is positioned as a key supplier of smart driving and cockpit solutions, providing essential features such as navigation, parking, and in-cabin functionalities, leveraging its full-stack self-research capabilities in algorithm development, software engineering, and hardware design [3][4]. - There is a rapid growth in demand for mid-to-high-level assisted driving solutions driven by the automotive industry's push for smart driving equality since 2025, with Youjia Innovation experiencing a significant year-on-year increase in projects [3][4]. - The demand for L4 autonomous driving projects has also surged this year, with successful deliveries of autonomous minibuses and project confirmations from major clients [4]. Group 3: Market Recognition and Investor Confidence - Youjia Innovation has received recognition from industry clients, including major automotive manufacturers, and has secured repeat orders for its iPilot 4 integrated driving assistance controller [4]. - The company has gained confidence from cornerstone investors, with commitments to limit share reductions post-lockup, contributing to stable stock performance following the end of the lockup period [4]. - Research reports from various securities firms have rated Youjia Innovation positively, with expectations of a compound annual growth rate of 49% in total revenue from fiscal years 2024 to 2027, and a target price of HKD 32.00 [5].

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15个 方向 学习 路线 世界模型Epona 地平线、清华、北大等团队ICCV'25中稿的自回归扩散世界模型工作，同时可以不依赖视频预测独立输出轨 迹规划。 主要贡献： 论文标题：Epona: Autoregressive Diffusion World Model for Autonomous Driving 论文链接：https://arxiv.org/abs/2506.24113 项目主页：https://kevin-thu.github.io/Epona/ 长时序生成。Epona可以实现长达2分钟的长时间生成，显著优于现有的世界模型； 实时轨迹规划。独立的多模态生成架构能够在视频预测不可用的情况下独立输出轨迹规划，从而显著降 低了推理FLOPS。这实现了高质量甚至实时的轨迹规划，高达20Hz的帧率； 视觉细节的保存。Epona的自回归公式采用连续视觉标记器而不是离散标记器，从而保留了丰富的场景 细节； 可视化： 算法框架： 实验结果： | Metric | | | | DriveGAN [30] DriveDreamer [5 ...

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15个 方向 学习 路线 今天自动驾驶之心为大家分享 澳门大学X 武汉大学 最新的工作！ 时序融合等价于 梯度下降？GDFusion 刷新 OCC 性能 SOTA，显存还大降72%！ 如果您有相关工 作需要分享，请在文末联系我们！ 自动驾驶课程学习与技术交流群事宜，也欢迎添加小助理微信AIDriver004做进一 步咨询 >>自动驾驶前沿信息获取 → 自动驾驶之心知识星球 论文作者 | Dubing Chen等 编辑 | 自动驾驶之心 一句话总结：来自澳门大学等机构的研究者提出了一种全新的时序融合框架GDFusion。它通过一个极其巧 妙的视角——将传统RNN更新过程重新诠释为"特征空间上的梯度下降"，成功统一了多种异构时序信息的 融合。GDFusion不仅在3D占用栅格预测任务上取得了1.4%-4.8%的mIoU提升，更惊人地将推理显存消耗 降低了27%-72%，实现了性能和效率的双赢。 论文标题 ：Rethinking Temporal Fusion with a Unified Gradient Descent View for ...

Group 1 - Pony AI is a recent IPO and a global leader in autonomous driving technology [1] - The development and commercialization of autonomous driving technology is still in the early stages of broad adoption [1] - There is significant greenfield opportunity for the industry [1]

Core Viewpoint - The article emphasizes the importance of 4D automatic annotation in the autonomous driving industry, highlighting the shift from traditional 2D static element annotation to more efficient 3D scene reconstruction methods [2][3][4]. Group 1: Traditional 2D Annotation Deficiencies - Traditional 2D static element annotation is time-consuming and labor-intensive, requiring repeated work for each timestamp [2]. - The need for 3D scene reconstruction allows for static elements to be annotated only once, significantly improving efficiency [2][3]. Group 2: 4D Automatic Annotation Process - The process of 4D automatic annotation involves several steps, including converting 3D scenes to BEV views and training cloud-based models for automatic annotation [6]. - The cloud-based pipeline is distinct from the vehicle-end model, focusing on high-quality automated annotation that can be used for vehicle model training [6]. Group 3: Challenges in Automatic Annotation - Key challenges in 4D automatic annotation include high temporal consistency requirements, complex multi-modal data fusion, and the difficulty of generalizing dynamic scenes [7]. - The industry faces issues with annotation efficiency and cost, as high-precision 4D automatic annotation often requires manual verification, leading to long cycles and high costs [7]. Group 4: Course Offerings and Learning Opportunities - The article promotes a course on 4D automatic annotation, covering dynamic and static elements, OCC, and end-to-end automation processes [8][9]. - The course aims to provide a comprehensive understanding of the algorithms and practical applications in the field of autonomous driving [8][9]. Group 5: Course Structure and Target Audience - The course is structured into multiple chapters, each focusing on different aspects of 4D automatic annotation, including dynamic obstacle marking, SLAM reconstruction, and end-to-end truth generation [9][11][12][16]. - It is designed for a diverse audience, including researchers, students, and professionals looking to transition into the data loop field [22][24].

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Core Viewpoint - 易控智驾科技股份有限公司 is positioned as a leading L4 autonomous driving solution provider globally, particularly in the mining sector, with significant commercial applications and a strong market presence [2][3]. Group 1: Company Overview - 易控智驾 was established in 2018 and has developed two main solutions: the "Zhuoshan" autonomous driving solution for mining and the "Muye" digitalization solution for smart mining [2]. - The "Zhuoshan" solution aims to facilitate autonomous transportation in mining under various working conditions, enhancing safety and operational efficiency [2]. - The "Muye" solution focuses on upgrading traditional mining equipment and workflows, enabling real-time decision-making between autonomous and manually operated mining sites [2]. Group 2: Market Position and Performance - According to Frost & Sullivan, 易控智驾 ranks first among global L4 autonomous driving companies based on projected revenue for 2024 [3]. - As of June 18, 2025, 易控智驾 has deployed over 1,400 active autonomous mining trucks, making it the largest provider of autonomous mining solutions globally [4]. - The company has maintained a 100% retention rate among its terminal customer groups from 2022 to 2024, with an average first-year vehicle expansion rate of 457% [4]. Group 3: Financial Performance - 易控智驾's revenue for the years 2022, 2023, and 2024 was 60 million, 271 million, and 986 million respectively, reflecting a compound annual growth rate of 305.8% [4]. - In 2024, the company achieved a gross margin of 7.6%, while the net loss margin stood at 39.5% [5]. Group 4: Future Plans - The funds raised from the IPO will primarily be used to enhance software and hardware development, support global business expansion, and seek strategic investments and potential acquisitions [5].