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 最近和业内专家讨论了导航信息SD如何应用到自动驾驶中，分享给大家： 图商提供的导航信息SD/SD Pro目前已经在很多量产方案上使用了。导航可以提供车道、粗粒度的waypoint等信息，相当于给司机提供了一个粗略的全局和局部视 野，将导航信息应用到车端模型上也就顺水渠成。目前来看，导航模块的核心职责有两个： 当然还有非常重要的一part，提供参考线reference line，这是下游规控强需的信息，有了参考线，可以极大的减轻规划的压力，相当于车辆已经有一条行驶的参考路 线，只需在细化即可。 除此之外，还可以提供规划约束与优先级、路径监控和重规划。 1. 车道级的全局路径规划：搜索一条目标车道的最优lane sequence； 2. 给行为规划提供明确的语义指导，方便车辆提前准备变道、减速、让行； 具体涉及到自车定位、道路结构构建和感知定位匹配可以参考下图： 在两段式中，导航输入到感知模型中，输出navi path，navi path作为ml planner的输入进而预测自车的行驶轨迹。 在一段式框架中，SD ...

London is set to become the next testing ground for global autonomous driving firms after Chinese tech group Baidu confirmed plans to introduce robotaxis in the UK capital from next year. The move, an... ...

Core Viewpoint - The article discusses the DiffusionDrive model, which utilizes a truncated diffusion approach for end-to-end autonomous driving, emphasizing its architecture and the integration of reinforcement learning to enhance trajectory planning and safety [1]. Group 1: Model Architecture - DiffusionDriveV2 incorporates reinforcement learning constraints within a truncated diffusion modeling framework for autonomous driving [3]. - The model architecture includes environment encoding through bird's-eye view (BEV) features and vehicle status, facilitating effective data processing [5]. - The trajectory planning module employs multi-scale BEV features to enhance the model's ability to predict vehicle trajectories accurately [8]. Group 2: Trajectory Generation - The model generates trajectories by first clustering true future trajectories of the vehicle using K-Means to create anchors, which are then perturbed with Gaussian noise to simulate variations [12]. - The trajectory prediction process involves cross-attention mechanisms that integrate trajectory features with BEV features, enhancing the model's predictive capabilities [15][17]. - The final trajectory is derived from the predicted trajectory offsets combined with the original trajectory, ensuring continuity and coherence [22]. Group 3: Reinforcement Learning and Safety - The Intra-Anchor GRPO method is proposed to optimize strategies within specific behavioral intentions, enhancing safety and goal-oriented trajectory generation [27]. - A comprehensive scoring system evaluates generated trajectories based on safety, comfort, rule compliance, progress, and feasibility, ensuring robust performance in various driving scenarios [28]. - The model incorporates a modified advantage estimation approach to provide clear learning signals, penalizing trajectories that result in collisions [30]. Group 4: Noise and Exploration - The model introduces multiplicative noise to maintain trajectory smoothness, addressing the inherent scale inconsistencies between proximal and distal trajectory segments [33]. - This approach contrasts with additive noise, which can disrupt trajectory integrity, thereby improving the quality of exploration during training [35]. Group 5: Loss Function and Training - The total loss function combines reinforcement learning loss with imitation learning loss to prevent overfitting and ensure general driving capabilities [39]. - The trajectory recovery and classification confidence contribute to the overall loss, guiding the model towards accurate trajectory predictions [42].

Core Insights - Waymo's latest blog discusses advancements in safety validation and explainability methods under a new end-to-end paradigm, the operational framework of its large-scale driving model, and the data flywheel concept [2][4][8] Group 1: Safety Validation and Explainability - The safety validation and explainability methods are closely tied to Waymo's foundational model, which operates on a dual system: a fast system focused on perception and a slow system based on a Vision-Language Model (VLM) [2][4] - The VLM is designed for complex semantic reasoning, utilizing rich camera data and fine-tuned on Waymo's driving data to handle rare and complex scenarios, such as navigating around a vehicle on fire [4][5][7] Group 2: Data Flywheel Concept - Waymo's data flywheel consists of an inner loop based on reinforcement learning for simulation-validation-vehicle integration and an outer loop based on real vehicle testing [8][11] - The insights from the data flywheel emphasize the importance of vehicle data mining and the reliance on world model-based generative simulations [12] Group 3: Foundation Model Applications - The foundational model serves three main purposes, including vehicle data extraction, cloud simulation, and evaluation for safety and explainability under the new paradigm [6][11] - The model's architecture allows for the transformation of vehicle trajectory prediction into a next-token prediction task, leveraging large language models for enhanced performance [5][11]

Group 1: General Motors - General Motors Co. is considering Sterling Anderson, its current Chief Product Officer and former Tesla Autopilot executive, as a potential successor to CEO Mary Barra [2] - Anderson's focus would be on enhancing hardware and software integration for GM's vehicles [2] Group 2: Tesla - Tesla's Full Self-Driving (FSD) system received positive feedback from South Korean lawmaker Lee So-young, who praised it as a game-changer [3] - The FSD system recently launched in South Korea, and there are plans for a European rollout [3] Group 3: Waymo - Alphabet Inc.'s autonomous driving unit, Waymo, is in discussions for a funding round that could exceed $10 billion, potentially valuing the company at $100 billion or more [5] Group 4: Ford - Ford Motor Company is reportedly shifting its focus away from electric vehicles (EVs) due to lower-than-expected demand [6] - RBC Capital Markets analyst Tom Narayan maintained a Sector Perform rating on Ford, commending the company's strategic restructuring [6] Group 5: Air Taxi Strategy - The Trump administration's strategy to initiate air taxi operations in the U.S. was unveiled by Transportation Secretary Sean Duffy, emphasizing the emergence of eVTOL aircraft and drones [4] - The strategy aims to position the U.S. as a leader in aviation and to compete with China [4]

编者按：2025年，经济观察报以"我们的四分之一世纪"为年终特刊主题，旨在通过数十位时代亲历者的 故事，共绘一幅属于这段岁月的集体记忆图谱。 二十一世纪的第一个25年即将结束之际，我问凯文·凯利（KK），有哪些发展超乎他的想象？又有哪些 低于他的预期？他简单地将答案归结为"意外之快""意外之慢"，以及"意外之路"。 这三大意外也让我们意识到，前瞻未来时，我们往往会低估创新者的颠覆性，因此必须跳出线性思维； 我们也会在一厢情愿中忽略木桶原理；当然，也会有意外之喜，因为另辟蹊径常常带来爆炸式的后果。 归根结底，未来既充满不确定性，也蕴藏诸多可能性，发现意外之美是最大的快乐。 一、 意外之快：智能手机的"非线性爆发" "我没想到智能手机会吃掉一切"——智能手机普及的速度与广度——是KK最直观的意外。2007年 iPhone问世时，多数人将其视为更精致的功能机；但短短十年间，它不仅完成了全球数十亿级的渗透， 更以吞噬一切的姿态重构了产业格局：相机、MP3、导航仪、钱包乃至电脑的功能，被压缩进方寸屏 幕；社交媒体、移动支付、网约车等新生态，借由手机的普及破土而出。这种"意外之快"，本质上是技 术融合催生的"非线性爆发" ...

CiDi, a provider of autonomous driving technology for commercial vehicles, has listed its shares in Hong Kong. Its CEO Albert Sibo Hu discusses the company's growth and international expansion strategy. He speaks with Yvonne Man on "Bloomberg: The China Show." ...

Core Insights - GAIA-3 represents a significant advancement in the evaluation of autonomous driving systems, transitioning world modeling from a visual synthesis tool to a foundational element for safety assessment [4][20] - The model combines the realism of real-world data with the controllability of simulations, enabling the generation of structured and purposeful driving scenarios for safety validation [6][20] Group 1: GAIA-3 Features - GAIA-3 is a powerful testing tool that can modify vehicle trajectories, weather conditions, and adapt to different sensor configurations [3] - It is built on a latent diffusion model with 15 billion parameters, doubling the video tokenizer size compared to its predecessor GAIA-2 [3][19] - The model allows for the generation of controlled variants of real-world driving sequences, maintaining consistency in the environment while altering vehicle behavior [6][8] Group 2: Safety and Evaluation - GAIA-3 addresses the limitations of traditional testing methods by generating systematic variations of critical safety scenarios, such as collisions, using real-world data metrics [7][8] - The model enables offline evaluation of autonomous systems by recreating unexpected events, allowing for quantitative testing of recovery capabilities in edge cases [9][20] - It emphasizes consistency in generated scenarios, ensuring that changes in vehicle behavior do not disrupt the physical and visual coherence of the environment [8][11] Group 3: Data Enrichment and Robustness - GAIA-3 enhances data coverage by generating structured variants from rare failure modes, facilitating targeted testing and retraining [12][13] - The model supports controlled visual diversity, allowing for measurable changes in appearance while keeping the underlying structure consistent, thus improving robustness assessments [11] - It can transfer scenarios across different sensor configurations, enabling data reuse across various vehicle projects without the need for paired collection [10] Group 4: Technical Advancements - The advancements in GAIA-3 are driven by increased scale, with training compute five times that of GAIA-2 and a dataset covering eight countries across three continents [16][19] - The model captures critical spatial and temporal structures, enhancing the fidelity of generated scenarios and improving the understanding of causal relationships in driving behavior [19][18] - GAIA-3's capabilities provide a reliable framework for structured, repeatable testing, marking a significant step towards scalable evaluation of end-to-end driving systems [20]

Core Viewpoint - Tesla's AI leader Ashok Elluswamy revealed the technical methodology behind Tesla's Full Self-Driving (FSD) in a recent article, emphasizing the choice of an end-to-end neural network model and addressing the challenges faced in practice [4][6]. Group 1: End-to-End Neural Network Model - Tesla's decision to adopt an end-to-end neural network model is driven by the need to address complex driving scenarios that cannot be pre-defined by rules, such as the "trolley problem" and second-order effects [6][10]. - The end-to-end model is described as a complete overhaul of previous architectures, fundamentally changing design, coding, and validation processes, leading to a more human-like driving experience [11][19]. - The model outputs driving instructions alongside interpretable "intermediate results," utilizing technologies like generative Gaussian splatting to create dynamic 3D models of the environment in real-time [8][17]. Group 2: VLA and World Model Concepts - VLA (Vision-Language-Action) is an extension of the end-to-end model that incorporates language information, allowing for a more visual representation of driving behavior [12][14]. - The world model aims to establish a high-bandwidth cognitive system based on video/image data, addressing the limitations of language models in understanding complex, dynamic environments [15][19]. - The relationship between end-to-end, VLA, and world models is clarified, with end-to-end serving as the foundation, VLA as an upgrade, and the world model as the ultimate form of understanding spatial dynamics [12][19]. Group 3: Industry Perspectives and Trends - The industry is divided into three main technical routes: end-to-end, VLA, and world model, with companies like Horizon Robotics and Bosch primarily adopting end-to-end due to lower costs and higher stability [13][19]. - VLA has faced criticism from industry leaders who argue that its reliance on language models may not be essential for effective autonomous driving, emphasizing the need for spatial understanding instead [16][19]. - Tesla's recent publication has reignited discussions in the industry, positioning the company at the forefront of current technological directions and providing a systematic analysis of practical applications [20].

Hong Kong's initial public offering (IPO) market is heading for its busiest month in four years, as a late rush of listings gathers pace despite the traditional slowdown around the Christmas and New Year holidays. At least 15 companies were set to go public by the end of December, with drug-discovery firm Insilico Medicine planning one of the largest deals in the final stretch of the year, according to data compiled by the Post. A total of 12 companies had already made their market debuts between December ...