Core Viewpoint - The article emphasizes the significance of the development of 3D Gaussian Splatting (3DGS) in the field of autonomous driving, highlighting its potential to enhance simulation capabilities and improve the efficiency of scene reconstruction [2][3]. Group 1: Development and Importance of 3DGS - The introduction of 3D Gaussian Splatting (3DGS) is seen as a major advancement, with Tesla's recent sharing indicating a shift towards end-to-end and generative approaches in autonomous driving [2]. - The evolution of 3DGS is outlined as a progression from static reconstruction to dynamic and mixed scene reconstruction, culminating in the feed-forward GS approach [3]. Group 2: Course Overview and Structure - A comprehensive course on 3DGS has been developed, covering theoretical foundations and practical applications, designed to aid beginners in understanding the complexities of the technology [3][8]. - The course is structured into six chapters, each focusing on different aspects of 3DGS, including background knowledge, principles and algorithms, and important research directions [8][9][10][11][12]. Group 3: Technical Highlights - Key features of the 3DGS approach include a unified network architecture that enhances training, inference, and testing, achieving real-time performance at a hundred milliseconds level [6]. - The integration of world models with 3DGS allows for improved closed-loop simulation capabilities, combining generation and reconstruction [6]. Group 4: Target Audience and Learning Outcomes - The course is aimed at individuals with a foundational understanding of computer graphics, visual reconstruction, and programming, providing them with the skills necessary for careers in both academia and industry [17]. - Participants will gain a thorough understanding of 3DGS theory, algorithm development frameworks, and the ability to engage with peers in the field [17].

Core Viewpoint - The article emphasizes the transition of autonomous driving from "perception-driven" to "spatial intelligence" by 2025, highlighting the importance of understanding and interacting with the three-dimensional physical world [3]. Group 1: Spatial Intelligence Definition - Spatial intelligence is defined as the ability to perceive, represent, reason, decide, and interact with spatial information, which is crucial for the interaction between intelligent agents and the physical world [3]. - Current spatial intelligence is primarily focused on perception and representation, with significant room for improvement in reasoning, decision-making, and interaction capabilities [3]. Group 2: World Models and Simulation - GAIA-2 is a multi-view generative world model for autonomous driving that generates driving videos based on physical laws and conditions, addressing edge cases in driving scenarios [5]. - GAIA-3 enhances GAIA-2 by increasing the scale fivefold and capturing fine-grained spatiotemporal contexts, representing the physical causal structure of the real world [9]. - ReSim combines expert trajectories from the real world with simulated dangerous behaviors to achieve high-fidelity simulations of extreme driving scenarios [11]. Group 3: Multimodal Reasoning - The SIG framework introduces a structured graph scheme that encodes scene layouts and object relationships, aiming to enhance geometric reasoning in autonomous driving [16]. - OmniDrive generates a large-scale 3D question-answer dataset to align visual language models with 3D spatial understanding and planning [19]. - SimLingo addresses the alignment of driving behavior with semantic instructions through an action dreaming task, demonstrating the potential of general models in real-time decision-making [21]. Group 4: Real-time Digital Twins - DrivingRecon is a 4D Gaussian reconstruction model that predicts parameters from surround-view videos, enabling efficient dynamic scene reconstruction for autonomous driving [26]. - VR-Drive enhances robustness in driving systems by allowing real-time prediction of new viewpoints without scene optimization [29]. Group 5: Embodied Fusion - MiMo-Embodied is the first open-source cross-embodied model that integrates autonomous driving with embodied intelligence, showcasing significant transfer effects in spatial reasoning capabilities [31]. - DriveGPT4-V2 is a closed-loop end-to-end autonomous driving framework that outputs low-level control signals, evolving from visual understanding to closed-loop control [36]. Group 6: Industry Trends - By 2025, the industry is moving towards an end-to-end VLA architecture, leveraging large language models for driving decision-making [40]. - Waymo's EMMA model integrates multimodal inputs and outputs in a unified language space, enhancing complex reasoning in driving tasks [41]. - DeepRoute.ai's DeepRoute IO 2.0 architecture introduces chain-of-thought reasoning to address the "black box" issue in end-to-end models, improving user trust in autonomous systems [44].

Core Viewpoint - The article discusses the development and evaluation of X-Driver, a unified multimodal large language model (MLLM) framework designed for closed-loop autonomous driving, emphasizing the importance of closed-loop evaluation metrics for assessing the performance of autonomous driving systems [2][3][23]. Group 1: Methodology and Architecture - X-Driver utilizes a CoT (Chain of Thought) reasoning mechanism integrated within the MLLM to enhance decision-making in autonomous driving, processing inputs from camera data and navigation commands [6][11]. - The system operates in a closed-loop manner, where actions taken by the vehicle affect the real-world environment, generating new sensory data for continuous optimization [7][24]. - The architecture includes LLaVA, a multimodal model that aligns features from images and text, ensuring a comprehensive understanding of driving scenarios [9][10]. Group 2: Training and Reasoning Process - The CoT fusion training method employs high-quality CoT prompt data to improve reasoning and decision-making capabilities in driving scenarios [11][12]. - The model breaks down tasks into sub-tasks such as object detection and traffic signal interpretation, integrating these results to generate final driving decisions [17][18]. - The training process includes accurate perception of complex 3D driving environments and adherence to traffic regulations, ensuring safe navigation [15][22]. Group 3: Closed-loop Evaluation and Results - The closed-loop evaluation is conducted using the CARLA simulation environment, focusing on Driving Score and Success Rate as key performance indicators [27][28]. - The Bench2Drive dataset, containing over 2 million frames, is utilized to assess the closed-loop driving performance under various conditions [27]. - Results indicate that incorporating CoT reasoning significantly improves decision accuracy, with the success rate for closed-loop simulations still around 20% [30][31].

Core Insights - The autonomous driving industry has experienced significant developments this year, focusing on technology, cost, and efficiency improvements as it matures [1] - There has been a notable shift in talent, with many professionals transitioning to other sectors like L4, embodiment, and drones, while algorithm talent in autonomous driving remains highly sought after [1][2] - Major technological advancements in autonomous driving have consolidated around key areas such as end-to-end systems, VLA, world models, and reinforcement learning, with many midstream companies actively hiring [3] Industry Trends - The autonomous driving sector is seeing an increase in B-end clients and a movement towards offline engagement, while C-end services are becoming more specialized [1] - The community of paid members in the autonomous driving sector has surpassed 4,000, indicating growing interest and engagement in technology development and job opportunities [3] - The industry is characterized by strong collaboration capabilities among professionals who have experience with large clusters and corner cases, which are lacking in other sectors [2]

Core Viewpoint - The article emphasizes the need for collaboration and innovation in the L2 intelligent driving sector, highlighting the importance of engaging more talented individuals to address industry challenges and contribute to advancements in technology [2]. Group 1: Industry Dynamics - The L2 intelligent driving sector is entering a critical phase where overcoming existing difficulties requires collective effort from industry professionals [2]. - The company aims to enhance its platform by providing various outputs such as roundtable discussions, practical and industrial-grade courses, and consulting services to add value to the industry [2]. Group 2: Key Directions - The main focus areas for development include but are not limited to: autonomous driving product management, 4D annotation/data closure, world models, VLA, large models for autonomous driving, reinforcement learning, and end-to-end solutions [4]. Group 3: Job Descriptions - The company is targeting training collaborations in autonomous driving, primarily focusing on B-end partnerships with enterprises, universities, and research institutions, as well as C-end offerings for students and job seekers [5].

Core Viewpoint - Waymo is advancing its autonomous driving technology by prioritizing "verifiable safe AI" as a core principle, significantly reducing accident rates compared to human drivers, with over 100 million miles of fully autonomous driving achieved [2][4]. Group 1: Waymo's AI Strategy - Waymo's AI ecosystem integrates a driver, simulator, and evaluator, all powered by the Waymo Foundation Model, ensuring safety is a foundational element rather than an afterthought [4][11]. - The Waymo Foundation Model serves as a multifunctional "world model," providing a robust framework for the AI ecosystem, enhancing interaction between components and supporting end-to-end signal backpropagation [7][9]. Group 2: Components of the AI Ecosystem - The driver model generates safe and compliant action sequences, with a distillation process transferring knowledge to more efficient student models for real-time deployment [13]. - The simulator creates high-fidelity virtual environments for training and testing the driver model, covering diverse and challenging scenarios [15][16]. - The evaluator system analyzes driving behavior, providing feedback for continuous improvement and ensuring the driver model's performance is rigorously tested [17]. Group 3: Learning and Optimization Mechanisms - Waymo's internal learning loop, powered by the simulator and evaluator, utilizes reinforcement learning to enhance the driver model's capabilities in a controlled environment [18]. - The external learning loop leverages real-world driving data to identify suboptimal behaviors, generating training data for the driver model, which is then validated through the simulator [20]. - This continuous learning cycle is supported by a vast amount of fully autonomous driving data, which is critical for ongoing optimization and cannot be replicated through simulation alone [20][21].

Core Viewpoint - NIO has experienced a remarkable turnaround in 2023, overcoming significant challenges and criticisms to achieve impressive sales milestones and operational improvements [5][10][20]. Group 1: Sales Performance - NIO's new ES8 model has achieved over 30,000 deliveries, setting a record for electric vehicles priced above 400,000 yuan [9]. - As of November 30, NIO's cumulative delivery volume reached 950,000 units, nearing the milestone of one million [13][14]. - In November, NIO delivered 36,275 vehicles, marking a year-on-year increase of 76.3%, with the NIO brand delivering 18,393 units [20]. Group 2: Organizational Changes - NIO's CEO, Li Bin, has implemented significant organizational changes, focusing on accountability and operational efficiency through a new CBU mechanism [10][25]. - The theme of NIO Day 2023 was "Growth," reflecting the company's transformation and renewed focus on operational effectiveness [24]. - NIO has restructured its business into 12 core operational units, each with clear performance targets [25]. Group 3: Market Trends - The market for high-end electric vehicles is rapidly expanding, with pure electric SUVs surpassing hybrid and fuel models in sales for the first time [36]. - The penetration rate of pure electric vehicles in the 300,000 yuan and above market has increased from 12% to 18% year-on-year [36]. - NIO's product offerings, including the ES8 and L90, align well with market trends, positioning the company for future growth [44]. Group 4: Future Outlook - NIO plans to launch three new models in 2024, which are expected to perform well in the expanding electric vehicle market [44]. - The company aims to achieve profitability in Q4 2023, with a target of delivering between 120,000 to 125,000 vehicles [22][31]. - NIO's extensive battery swap network, with 3,631 stations, enhances its competitive advantage and addresses consumer concerns about charging [44].

本文只做学术分享，如有侵权，联系删文 哼哧哼哧搞了小半年，小结一下这段时间的学习成果。 什么是世界模型？ 值得注意的是，世界模型不是一个具体的模型或者范式。实际上有好几个不同方向的都管自己叫世界模型。差不多是各说各的，因此大家在阅读文章时需要仔细辨 析。 World model 的流行要归功于Jurgen2018年的world .其对world model的定义是" a mental model of the world", 即世界在大脑中的映射。更具体一点是 作者 | cloud erow 编辑 | 自动驾驶之心 原文链接： https://zhuanlan.zhihu.com/p/1943329007706805619 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 The image of the world around us, which we carry in our head, is just a model. Nobody in his head imagines all the worl ...

Core Insights - The article discusses various cutting-edge directions in autonomous driving research, emphasizing the importance of deep learning and traditional methods for students in related fields [2][3]. Group 1: Research Directions - Key areas of focus include VLA, end-to-end learning, reinforcement learning, 3D goal detection, and occupancy networks, which are recommended for students in computer science and automation [2][3]. - For mechanical and vehicle engineering students, traditional methods like PnC and 3DGS are suggested as they require lower computational power and are easier to start with [2]. Group 2: Guidance and Support - The article announces the launch of a paper guidance service that offers support in various research areas, including multi-sensor fusion, trajectory prediction, and semantic segmentation [3][6]. - Services provided include topic selection, full process guidance, and experimental support, aimed at enhancing the research capabilities of students [6][7]. Group 3: Publication Opportunities - The guidance service has a high acceptance rate for papers submitted to top conferences and journals, including CVPR, AAAI, and ICLR [7]. - The article highlights the availability of support for various publication levels, including CCF-A, CCF-B, and SCI indexed journals [10].

Core Viewpoint - The article critiques the VLA (Visual Language Agent) approach, emphasizing that while it has merits, it also has significant limitations that need to be addressed for better performance in complex environments [1]. Group 1: Challenges and Limitations - The main challenge lies in enabling models to generalize effectively [2]. - Current models struggle in complex environments due to simplistic task settings, often limited to "grab-and-drop" scenarios with minimal obstacles [6]. - The reliance on large datasets and the black-box nature of systems hinder understanding of model capabilities [6]. Group 2: Proposed Solutions - A focus on designing effective subgoal embeddings is crucial for ensuring generalization, potentially using cross-attention mechanisms to link task text tokens with image patch tokens [3][4]. - The article suggests that learning-based methods may outperform traditional methods in complex environments, as they can adapt to visual observation errors and continuously correct actions [4]. - An explicit VLA approach is recommended, where large models break down tasks into subgoals, allowing for clearer structure and reduced training requirements [8].