Core Insights - WeRide has received the first fully autonomous Robotaxi license in Switzerland, allowing it to operate on public roads in the Furttal area of Zurich [2][3] - The company is now the only tech firm globally with autonomous driving licenses in eight countries, marking a significant milestone in its globalization strategy [3] Business Development - The Robotaxi will be part of the iamo (Intelligent Automated Mobility) pilot project, aimed at integrating autonomous vehicles into public transport systems to enhance local mobility and promote sustainable transport [3] - The operational area for the project will cover approximately 110 kilometers with around 460 stops, and vehicles can reach speeds of up to 80 km/h [3] Testing and Implementation - WeRide has partnered with a local driving school to conduct autonomous driving tests with safety personnel on board, covering various regions in Furttal [4] - After successful testing with the Swiss Federal Roads Office (FEDRO), the company plans to initiate fully autonomous driving tests monitored remotely by Eurobus, Switzerland's largest private bus operator [6] Future Plans - WeRide aims to officially launch its Robotaxi service to the public in Switzerland by mid-2026, with plans to gradually incorporate Robobus vehicles into its fleet [6] - Starting from June 2025, the Robobus will provide shuttle services for Zurich airport employees, with training for safety personnel and remote operation set to begin in October 2025 [6]

Core Viewpoint - The strategic partnership between Qingzhou Zhihang and Zhejiang Jinmail Supply Chain Technology Service Co., Ltd. aims to promote the large-scale commercial use of L4 autonomous logistics vehicles on open roads in Ningbo by 2025 [1]. Group 1 - The two companies have signed a strategic cooperation agreement to facilitate the deployment of autonomous logistics vehicles [1]. - The plan includes the establishment of multiple benchmark routes for autonomous logistics operations in Ningbo by 2025 [1]. - The focus will be on the collection, delivery, and transfer of goods between distribution points and service stations [1]. Group 2 - The companies intend to deploy hundreds of autonomous logistics vehicles in the first half of 2026 [1].

Core Viewpoint - The strategic partnership between Lightyear Zhihang and Zhejiang Jinpost Supply Chain Technology Service Co., Ltd. aims to promote the large-scale commercial use of L4 autonomous logistics vehicles on open roads in Ningbo by 2025 [1] Group 1 - The agreement was signed on November 20, indicating a formal collaboration between the two companies [1] - The plan includes the establishment of multiple benchmark routes for autonomous logistics operations in Ningbo by 2025, focusing on the delivery and transfer of goods between distribution points and stations [1] - The companies intend to deploy hundreds of autonomous logistics vehicles in the first half of 2026 [1]

Core Insights - The article emphasizes the importance of end-to-end production in the automotive industry, highlighting the scarcity of qualified talent in this area [1][3] - A newly designed advanced course on end-to-end production has been developed to address the industry's needs, focusing on practical applications and real-world scenarios [3][5] Course Overview - The course covers essential algorithms such as one-stage and two-stage end-to-end frameworks, reinforcement learning applications, and trajectory optimization techniques [5][10] - It aims to provide hands-on experience and insights into production challenges, making it suitable for individuals looking to advance or transition in their careers [5][18] Course Structure - Chapter 1 introduces the overview of end-to-end tasks, focusing on the integration of perception and control algorithms [10] - Chapter 2 discusses the two-stage end-to-end algorithm framework, including its modeling and information transfer methods [11] - Chapter 3 covers the one-stage end-to-end algorithm framework, emphasizing its advantages in information transmission [12] - Chapter 4 focuses on the application of navigation information in autonomous driving, detailing map formats and encoding methods [13] - Chapter 5 introduces reinforcement learning algorithms, highlighting their necessity alongside imitation learning [14] - Chapter 6 provides practical experience in trajectory output optimization, combining imitation and reinforcement learning [15] - Chapter 7 discusses fallback strategies for trajectory smoothing and reliability in production [16] - Chapter 8 shares production experiences from various perspectives, including data and model optimization [17] Target Audience - The course is designed for advanced learners with a foundational understanding of autonomous driving algorithms, reinforcement learning, and programming skills [18][19] Course Logistics - The course starts on November 30 and spans three months, featuring offline video lectures and online Q&A sessions [20]

Core Insights - The article discusses the advancements in autonomous driving technology, particularly focusing on the development and implementation of VLA (Vehicle-Language Architecture) by Xiaopeng Motors, highlighting its significance in the industry [14]. Group 1: Company Developments - Xiaopeng Motors has announced the launch of VLA 2.0, which represents a significant step in the evolution of autonomous driving technology, transitioning from perception-based systems to more integrated approaches [14]. - The article reflects on a year of research and development in VLA, indicating a shift in focus from traditional perception methods to VLA, which aims to enhance the vehicle's decision-making capabilities [14]. Group 2: Industry Trends - The article notes a growing trend in the industry towards end-to-end autonomous driving solutions, with VLA being positioned as a potential game-changer in how vehicles interact with their environment [14]. - There is a discussion on the competitive landscape, particularly the debate between world models and VLA routes, suggesting that the industry is at a crossroads in terms of technological direction [14]. Group 3: Research and Academic Contributions - The article mentions recent academic contributions, such as the paper from The Chinese University of Hong Kong (Shenzhen) and Didi, which proposes a new method for dynamic driving scene reconstruction, indicating ongoing research efforts in the field [14].

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 >>直播和内容获取转到 → 自动驾驶之心知识星球 点击按钮预约直播 近年来，3DGS在自动驾驶领域的城市场景重建中展现出巨大潜力。然而，当前的城市场景重建方法通常依赖于多模态传 感器输入，例如激光雷达和图像。尽管激光雷达点云提供的几何先验在很大程度上可以缓解重建中的不适定性，但在实 践中获取如此精确的激光雷达数据仍然具有挑战性： i）需要对激光雷达与其他传感器之间进行精确的时空标定，因为它们可能并非同时捕获数据；ii）当激光雷达和相机安 装在不同位置时，空间未对准会导致重投影误差。为了避免获取精确激光雷达深度的困难，本文提出了 D²GS， 一种无需 激光雷达的城市场景重建框架。 在这项工作中，获得了与激光雷达效果相当，但更密集、更精确的几何先验。本次自动 驾驶之心为大家邀请到D²GS作者 博世创新软件中心，三维重建算法专家张友健 为大家详细分析本篇工作。 传统的自驾场景重建方案依赖激光雷 达作输入,往往会遇到标定误差和深 度投影误差等问题。对此，博世合成 数据团队提出,通过多视图深度初始 化高斯点云，并在训练 ...

Overview - The article discusses the ongoing technological competition in the autonomous driving industry, focusing on different approaches to solving corner cases and enhancing safety and efficiency in driving systems [1][3]. Technological Approaches - There is a debate between two main technological routes: single-vehicle intelligence (VLA) and intelligent networking (VLM) [1]. - Major companies like Waymo utilize VLM, which allows AI to handle environmental understanding and reasoning, while traditional modules maintain decision-making control for safety [1]. - Companies such as Tesla, Geely, and XPeng are exploring VLA, aiming for AI to learn all driving skills through extensive data training for end-to-end decision-making [1]. Sensor and Algorithm Developments - The article highlights the evolution of perception technologies, with BEV (Bird's Eye View) perception becoming mainstream by 2022, and OCC (Occupancy) perception gaining traction in 2023 [3][5]. - BEV integrates various sensor data into a unified spatial representation, facilitating better path planning and dynamic information fusion [8][14]. - OCC perception provides detailed occupancy data, clarifying the probability of space being occupied over time, which enhances dynamic interaction modeling [6][14]. Modular and End-to-End Systems - Prior to the advent of multimodal large models and end-to-end autonomous driving technologies, perception and prediction tasks were typically handled by separate modules [5]. - The article outlines a phased approach to modularization, where perception, prediction, decision-making, and control are distinct yet interconnected [4][31]. - End-to-end systems aim to streamline the process by allowing direct mapping from raw sensor inputs to actionable outputs, enhancing efficiency and reducing bottlenecks [20][25]. VLA and VLM Frameworks - VLA (Visual-Language-Action) and VLM (Visual-Language Model) frameworks are discussed, with VLA focusing on understanding complex scenes and making autonomous decisions based on visual and language inputs [32][39]. - The article emphasizes the importance of language models in enhancing the interpretability and safety of autonomous driving systems, allowing for better cross-scenario knowledge transfer and decision-making [57]. Future Directions - The competition between VLA and WA (World Action) architectures is highlighted, with WA emphasizing direct visual-to-action mapping without language mediation [55][56]. - The article suggests that the future of autonomous driving will involve integrating world models that understand physical laws and temporal dynamics, addressing the limitations of current language models [34][54].

Group 1 - The Goldman Sachs Group, Inc. increased its stake in 小马智行-W (02026.HK) by purchasing 1,182,800 shares at an average price of HKD 96.5407 per share, totaling approximately HKD 114 million [1] - Following this transaction, The Goldman Sachs Group, Inc.'s total holdings in 小马智行-W rose to 25,090,336 shares, increasing its ownership percentage from 6.78% to 7.12% [1]

Core Insights - The CTO of Qingsong Zhihang, Li Dong, announced that their technology has been integrated into over 20 vehicle models, with total software deployment expected to exceed 1 million units [1] - Li Dong emphasized that the industry is currently at a stage where advanced driver assistance systems (ADAS) are being fully implemented, and large-scale production of Level 4 (L4) autonomous driving is anticipated in the future [1] - Qingsong Zhihang's technology is characterized by the use of a unified technology stack that supports both Level 2 (L2) and L4 operations, allowing for shared operational experience and safety redundancies to enhance both levels [1]

Core Viewpoint - WeRide has received a fully autonomous license for its Robotaxi service in Switzerland, marking a significant milestone in the autonomous driving industry and enhancing its global presence in the market [1] Company Summary - WeRide's Robotaxi has officially obtained a fully autonomous license from the Swiss Federal Roads Office, allowing it to operate without a driver on public roads in the Zurich area [1] - This license is the first of its kind issued in Switzerland for a fully autonomous Robotaxi [1] - WeRide is now the only technology company globally that holds autonomous driving licenses in eight countries, including Switzerland, China, UAE, Saudi Arabia, Singapore, France, Belgium, and the United States [1] Market Reaction - Following the announcement, WeRide's stock price increased by over 7%, reaching $7.68 [1]