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 ...

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 随着神经场景表征的发展，之前出现了一些方法尝试用神经辐射场重建街道场景，像Block-NeRF 。但是它无法处理街道上的动态车辆，而这是自动驾驶环境仿真 中的关键要素。最近一些方法提出将动态驾驶场景表示为由前景移动汽车和静态背景组成的组合神经表示。为了处理动态移动的目标车辆，这些方法利用跟踪的 车辆姿态来建立观察空间和规范空间之间的映射，在那里他们使用 NeRF 网络来模拟汽车的几何形状和外观。虽然这些方法产生了合理的结果，但它们仍然局限 于高训练成本和低渲染速度。基于这些前述工作，浙大提出了S treet Gaussians。笔者有幸参与了公司新一代闭环仿真系统的开发，花了几个月的时间，终于把基 于Street Gaussians的算法落地。今天就分享下自己的一些看法~ 下图是在Waymo数据集上的渲染结果。street gaussians的方法在训练半小时内以 135 FPS的速度产生高质量的分辨率为1066×1600渲染视角。这两个基于NeRF的方 法存在训练和渲染成本高的问题。 以前的方法通常面临训练 ...

智能驾驶及智能座舱两大核心业务在今年上半年收获多个定点项目。 本文为IPO早知道原创 作者｜ Stone Jin 微信公众号｜ipozaozhidao 据 IPO早知道消息， 佑驾创新（ 2431.HK） 于 7月3日 发布公告，拟以每股 23.26港元配售680 万股，募资约1.58亿港元。配股价较上一日收市价27.30港元折让约14.80%，配售股份相当于经扩 大化后已发行股份数目约1.67%。 公告显示，假设所有配售股份获系数配售予承配人，配售事项的所得款项净额（经扣除佣金及估计开 支后）预期合共约 1.55亿港元。其中，40%将用于提升智能驾驶解决方案的功能表现和满足中高阶 辅助驾驶项目规模化落地需求；30%用作L4级自动驾驶解决方案的技术升级，支持载人载货自动驾 驶场景的商业化落地；20%用于探索潜在的战略伙伴、联盟及收购机会；10%用作运营资金。 佑驾创新 在公告中表示，其作为一家 智能驾驶及座舱解决方案供应商，为驾驶体验的关键环节提供 包括领航、泊车和舱内功能在内的解决方案。 基于 持续迭代的创新技术和高效稳定的量产能力，佑 驾创新持续为智能汽车赋能，助力行业快速实现智能驾驶大规模商业化。 此外 ...

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 Insights - Alphabet Inc. is facing increasing scrutiny and competition, particularly in its autonomous driving unit, Waymo, and its core productivity suite, Google Workspace [2][9][10] - The company reported strong financial performance in Q1 2025, with revenue of $90.24 billion and EPS of $2.81, but must navigate significant challenges to maintain its market position [13] Group 1: Waymo and Autonomous Driving - Waymo aims to create a fully autonomous driving system, with millions of miles driven on public roads and services launched in Phoenix and San Francisco, now expanding to Los Angeles and Austin [3][4] - The long-term potential for autonomous ride-hailing is substantial, with the possibility of multi-billion-dollar revenue streams, but monetization remains limited and public perception poses challenges [4][5] - Tesla's rapid rollout of its robotaxi program presents a direct threat to Waymo, with Tesla's model allowing car owners to participate in ride-hailing, potentially scaling faster and achieving profitability sooner [6][7][8] Group 2: Competition and Market Dynamics - OpenAI's plans to develop a new workspace productivity platform could challenge Google Workspace, which is crucial for Alphabet's revenue and supports its advertising ecosystem [9][10][11] - If OpenAI's platform proves to be more innovative, it could disrupt Alphabet's enterprise market share over time, impacting the company's overall ecosystem [11][12] Group 3: Financial Performance and Future Outlook - Alphabet's stock forecast indicates a potential upside of 12.71%, with a target price of $199.95 based on analyst ratings [12] - The company must defend its core businesses against emerging competitors while converting long-term investments like Waymo into growth drivers to avoid falling behind [14]

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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