Core Viewpoint - The article discusses the evolution of Bird's Eye View (BEV) perception as a foundational technology for autonomous driving, highlighting its importance in ensuring safety and reliability in complex driving environments [2][4]. Group 1: Essence of BEV Perception - BEV perception is an efficient spatial representation paradigm that projects heterogeneous data from various sensors (like cameras, LiDAR, and radar) into a unified BEV coordinate system, facilitating a consistent structured spatial semantic map [6][12]. - This top-down view significantly reduces the complexity of multi-view and multi-modal data fusion, aiding in the accurate perception and understanding of spatial relationships between objects [6][12]. Group 2: Importance of BEV Perception - With a unified and interpretable spatial representation, BEV perception serves as an ideal foundation for multi-modal fusion and multi-agent collaborative perception in autonomous driving [8][12]. - The integration of heterogeneous sensor data into a common BEV plane allows for seamless alignment and integration, enhancing the efficiency of information sharing between vehicles and infrastructure [8][12]. Group 3: Implementation of BEV Perception - The evolution of safety-oriented BEV perception (SafeBEV) is categorized into three main stages: SafeBEV 1.0 (single-modal vehicle perception), SafeBEV 2.0 (multi-modal vehicle perception), and SafeBEV 3.0 (multi-agent collaborative perception) [12][17]. - Each stage represents advancements in technology and features, addressing the increasing complexity of dynamic traffic scenarios [12][17]. Group 4: SafeBEV 1.0 - Single-Modal Vehicle Perception - This stage utilizes a single sensor (like a camera or LiDAR) for BEV scene understanding, with methods evolving from homography transformations to data-driven BEV modeling [13][19]. - The performance of camera-based methods is sensitive to lighting changes and occlusions, while LiDAR methods face challenges with point cloud sparsity and performance degradation in adverse weather [19][41]. Group 5: SafeBEV 2.0 - Multi-Modal Vehicle Perception - Multi-modal BEV perception integrates data from cameras, LiDAR, and radar to enhance performance and robustness in challenging conditions [42][45]. - Fusion strategies are categorized into five types, including camera-radar, camera-LiDAR, radar-LiDAR, camera-LiDAR-radar, and temporal fusion, each leveraging the complementary characteristics of different sensors [42][45]. Group 6: SafeBEV 3.0 - Multi-Agent Collaborative Perception - The development of Vehicle-to-Everything (V2X) technology enables autonomous vehicles to exchange information and perform joint reasoning, overcoming the limitations of single-agent perception [15][16]. - Collaborative perception aggregates multi-source sensor data in a unified BEV space, facilitating global environmental modeling and enhancing safety navigation in dynamic traffic [15][16]. Group 7: Challenges and Future Directions - The article identifies key challenges in open-world scenarios, such as open-set recognition, large-scale unlabeled data, sensor performance degradation, and communication delays among agents [17]. - Future research directions include the integration of BEV perception with end-to-end autonomous driving systems, embodied intelligence, and large language models [17].

Core Viewpoint - The article discusses the development and features of the PLUTO model within the end-to-end autonomous driving domain, emphasizing its unique two-stage architecture and its direct encoding of structured perception outputs for downstream control tasks [1][2]. Summary by Sections Overview of PLUTO - PLUTO is characterized by its three main losses: regression loss, classification loss, and imitation learning loss, which collectively contribute to the model's performance [7]. - Additional auxiliary losses are incorporated to aid model convergence [9]. Course Introduction - The article introduces a new course titled "End-to-End and VLA Autonomous Driving," developed in collaboration with top algorithm experts from domestic leading manufacturers, aimed at addressing the challenges faced by learners in this rapidly evolving field [12][15]. Learning Challenges - The course addresses the difficulties learners face due to the fast-paced development of technology and the fragmented nature of knowledge across various domains, making it hard for beginners to grasp the necessary concepts [13]. Course Features - The course is designed to provide quick entry into the field, build a framework for research capabilities, and combine theory with practical applications [15][16][17]. Course Outline - The course consists of several chapters covering topics such as the history and evolution of end-to-end algorithms, background knowledge on various technologies, and detailed discussions on both one-stage and two-stage end-to-end methods [20][21][22][29]. Practical Application - The course includes practical assignments, such as RLHF fine-tuning, allowing students to apply their theoretical knowledge in real-world scenarios [31]. Instructor Background - The instructor, Jason, has a strong academic and practical background in cutting-edge algorithms related to end-to-end and large models, contributing to the course's credibility [32]. Target Audience and Expected Outcomes - The course is aimed at individuals with a foundational understanding of autonomous driving and related technologies, with the goal of elevating their skills to the level of an end-to-end autonomous driving algorithm engineer within a year [36].

VLA绝对是今年自动驾驶的主流关键词，下半年新势力都在抢滩VLA的高地，工业界快速量产上 车，学术界不断刷新比赛榜单。 以往，业内迭代的方案都是增加issue case删除issue case的循环，而 这种方案显然是无穷无尽的，哪怕这个方案迭代的再成熟，也难以达到我们理想中那种自驾的水 准。 相比于端到端， 利用大模型更强的泛化能力， VLA至少提供了一种摆脱无尽corner case的可能性！ 然而VLA并不是那么好做的，对于一个新手或者转行的同学，开展研究蛮难受的。踩了一年坑，也 不一定能有效果。这时候，峰哥给他推荐了自动驾驶之心的1v6论文辅导。 ⼀、VLA科研论文辅导课题来啦⭐ 端到端（End-to-End）自动驾驶旨在构建一个统一的智能模型，直接将传感器原始输入（如摄像头图 像）映射到车辆的驾驶控制指令（如转向、油门、刹车），从而替代传统的多模块、级联式架构 （感知、预测、规划、控制）。这一演进过程大致可分为以下几个阶段，而VLA模型的出现正是为 了解决前序阶段的瓶颈，标志着一个新范式的开启。 刹车"，而不是理解"前车减速，所以要刹车"。 泛化能力受限： 对于训练数据中未出现过的长尾 场景，模型表 ...

最近，大模型在自动驾驶领域也逐渐崭露头角，像视觉-语言模型（VLM）和视觉-语言-动作模型（VLA）已经在理解场景、语义关联和泛化能力上有了不错的表现。不 过，这类模型在实际连续控制场景中还受一些限制，比如推理速度慢、动作不够连贯，以及安全性保障难度大。 与此同时，扩散模型（Diffusion Models）正在改变视觉、音频和控制领域的生成式建模方式。和传统的回归或分类方法不同，扩散策略（Diffusion Policy, DP）把动作生 成看作一个"逐步去噪"的过程，不仅能更好地表达多种可能的驾驶选择，还能保持轨迹的时序一致性和训练的稳定性。不过，这类方法在自动驾驶中还没被系统化研究 过。扩散策略通过直接建模输出动作空间，为生成平滑可靠的驾驶轨迹提供了一种更强大、更灵活的思路，非常适合解决驾驶决策中的多样性和长期稳定性问题。 另一方面，专家混合（MoE, Mixture of Experts）技术也逐渐成为大模型的重要架构。它通过按需激活少量专家，让模型在保持计算效率的同时具备更强的扩展性和模块化 能力。MoE 在自动驾驶中也被尝试应用，比如做多任务策略和模块化预测，但大多数设计还是面向具体任务，限制了专 ...

Core Viewpoint - The article discusses the current state and future prospects of end-to-end autonomous driving, emphasizing the concept of a "World Engine" to address challenges in the field [2][21]. Definition of End-to-End Autonomous Driving - End-to-end autonomous driving is defined as "learning a single model that directly maps raw sensor inputs to driving scenarios and outputs control commands," replacing traditional modular pipelines with a unified function [3][6]. Development Roadmap of End-to-End Autonomous Driving - The evolution of end-to-end autonomous driving has progressed from simple black-and-white image inputs over 20 years to more complex methods, including conditional imitation learning and modular approaches [8][10]. Current State of End-to-End Autonomous Driving - The industry is currently in the "1.5 generation" phase, focusing on foundational models and addressing long-tail problems, with two main branches: the World Model (WM) and Visual Language Action (VLA) [10][11]. Challenges in Real-World Deployment - Collecting data for all scenarios, especially extreme cases, remains a significant challenge for achieving Level 4 (L4) or Level 5 (L5) autonomous driving [17][18]. Concept of the "World Engine" - The "World Engine" concept aims to learn from human expert driving and generate extreme scenarios for training, which can significantly reduce costs associated with large fleets [21][24]. Data and Algorithm Engines - The "World Engine" consists of a Data Engine for generating extreme scenarios and an Algorithm Engine, which is still under development, to improve and train end-to-end algorithms [24][25].

Core Viewpoint - The article discusses the significance and application of Diffusion Models in various fields, particularly in autonomous driving, emphasizing their ability to denoise and generate data effectively [1][2][11]. Summary by Sections Introduction to Diffusion Models - Diffusion Models are generative models that focus on denoising, learning the distribution of data through a forward diffusion process and a reverse generation process [2][4]. - The concept is illustrated through the analogy of ink dispersing in water, where the model aims to recover the original data from noise [2]. Applications in Autonomous Driving - In the field of autonomous driving, Diffusion Models are utilized for data generation, scene prediction, perception enhancement, and path planning [11]. - They can handle both continuous and discrete noise, making them versatile for various decision-making tasks [11]. Course Offering - The article promotes a new course on end-to-end and VLA (Vision-Language Alignment) algorithms in autonomous driving, developed in collaboration with top industry experts [14][17]. - The course aims to address the challenges faced by learners in keeping up with rapid technological advancements and fragmented knowledge in the field [15][18]. Course Structure - The course is structured into several chapters, covering topics such as the history of end-to-end algorithms, background knowledge on VLA, and detailed discussions on various methodologies including one-stage and two-stage end-to-end approaches [22][23][24]. - Special emphasis is placed on the integration of Diffusion Models in multi-modal trajectory prediction, highlighting their growing importance in the industry [28]. Learning Outcomes - Participants are expected to achieve a level of understanding equivalent to one year of experience as an end-to-end autonomous driving algorithm engineer, mastering key frameworks and technologies [38][39]. - The course includes practical components to ensure a comprehensive learning experience, bridging theory and application [19][36].

Group 1 - The article introduces a significant learning package for the new academic season, including a 299 yuan discount card that offers a 30% discount on all platform courses for one year [3][5]. - Various course benefits are highlighted, such as a 1000 yuan purchase giving access to two selected courses, and discounts on specific classes and hardware [3][6]. - The focus is on cutting-edge autonomous driving technologies for 2025, particularly end-to-end (E2E) and VLA (Vision-Language Alignment) autonomous driving systems [5][6]. Group 2 - End-to-end autonomous driving is emphasized as a core algorithm for mass production, with a notable mention of the competition sparked by the UniAD paper winning the CVPR Best Paper award [6][7]. - The article discusses the rapid evolution of technology in the field, indicating that previous learning materials may no longer be suitable for current industry standards [7]. - The challenges faced by beginners in understanding fragmented knowledge and the lack of high-quality documentation in end-to-end autonomous driving research are addressed [7][8]. Group 3 - The article outlines specific courses aimed at addressing the complexities of autonomous driving, including a small class on 4D annotation algorithms, which are crucial for training data generation [11][12]. - The importance of automated 4D annotation in enhancing the efficiency of data loops and improving the generalization and safety of autonomous driving systems is highlighted [11]. - The introduction of a multi-modal large model and practical courses in autonomous driving is noted, reflecting the growing demand for skilled professionals in this area [15][16]. Group 4 - The article features expert instructors for the courses, including Jason, a leading algorithm expert in the industry, and Mark, a specialist in 4D annotation algorithms [8][12]. - The curriculum is designed to provide a comprehensive learning experience, addressing real-world challenges and preparing students for job opportunities in the autonomous driving sector [23][29]. - The article emphasizes the importance of community engagement and support through dedicated VIP groups for course participants, facilitating discussions and problem-solving [29].

小林是某C9高校的研二同学，目前实验室主要是做自动驾驶和机器人方向的。这两周刚开学，忙完 了寝室和班里里面杂七杂八的事情，该去实验室和老板Meeting一下了。老板这个暑假没闲着啊，看 了企业不少VLA都量产上车了，说咱们实验室也可以搞搞看，发发论文。 而传统的BEV感知、车道线、Occupancy等工作相对较少出现在顶会了，最近也有很多同学陆续来咨 询峰哥，传统的感知、规划这块还能继续发论文吗？感觉工作都已经被做的七七八八了，审稿人会 打高分吗？ 确实自动驾驶最近的热点都在大模型和VLA靠拢，然而VLA并不是那么好做的，对于一个新手或者 转行的同学，开展研究蛮难受的。踩了一年坑，也不一定能有效果。这时候，峰哥给他推荐了自动 驾驶之心的1v6论文辅导。 ⼀、VLA科研论文辅导课题来啦⭐ 端到端（End-to-End）自动驾驶旨在构建一个统一的智能模型，直接将传感器原始输入（如摄像头图 像）映射到车辆的驾驶控制指令（如转向、油门、刹车），从而替代传统的多模块、级联式架构 （感知、预测、规划、控制）。这一演进过程大致可分为以下几个阶段，而VLA模型的出现正是为 了解决前序阶段的瓶颈，标志着一个新范式的开启。 1. ...

传统的融合方式主要分为三种：早期融合直接在输入端拼接原始数据，但计算量巨大；中期融合则是在传感器数 据经过初步特征提取后，将不同模态的特征向量进行融合，这是目前的主流方案，例如将所有传感器特征统一到 BEV 视角下进行处理，这解决了不同传感器数据空间对齐的难题，并与下游任务无缝连接；后融合则是每个传 感器独立完成感知，最后在决策层面进行结果融合，可解释性强但难以解决信息冲突。 在这些基础上， 基于Transformer的端到端融合是当前最前沿的方向 。这种架构借鉴了自然语言处理和计算机 视觉领域的成功经验，通过其跨模态注意力机制，能够学习不同模态数据之间的深层关系，实现更高效、更鲁棒 的特征交互。这种端到端的训练方式减少了中间模块的误差累积，能够直接从原始传感器数据输出感知结果，如 3D目标框，从而更好地捕捉动态信息并提升整体性能。 我们了解到， 不少在读的研究生和博士生都在主攻多模态感知融合方向 ，前面我们推出了端到端和VLA方向的 1V6小班课，很多同学也在咨询我们多传感器融合方向，急需大佬辅导...... 模态感知融 科研2 7 课题背景 为克服单一传感器局限,多模态融合技术通过结合 激光雷达、毫米波雷 ...

Group 1 - The article introduces a significant learning package for the new academic season, including a 299 yuan discount card that offers a 30% discount on all platform courses for one year [3][5]. - Various course benefits are highlighted, such as a 1000 yuan purchase giving access to two selected courses, and discounts on specific classes and hardware [3][6]. - The focus is on cutting-edge autonomous driving technologies for 2025, particularly end-to-end (E2E) and VLA (Vision-Language Alignment) autonomous driving systems [5][6]. Group 2 - End-to-end autonomous driving is emphasized as a core algorithm for mass production, with a notable mention of the competition sparked by the UniAD paper winning the CVPR Best Paper award [6][7]. - The article discusses the challenges faced by beginners in mastering multi-modal large models and the fragmented nature of knowledge in the field, which can lead to discouragement [7][8]. - A course on automated 4D annotation algorithms is introduced, addressing the increasing complexity of training data requirements for autonomous driving systems [11][12]. Group 3 - The article outlines a course on multi-modal large models and practical applications in autonomous driving, reflecting the rapid growth and demand for expertise in this area [15][16]. - It mentions the increasing job opportunities in the field, with companies actively seeking talent and offering competitive salaries [15][16]. - The course aims to provide a systematic learning platform, covering topics from general multi-modal large models to fine-tuning for end-to-end autonomous driving applications [16][18]. Group 4 - The article emphasizes the importance of community and communication in the learning process, with dedicated VIP groups for course participants to discuss challenges and share insights [29]. - It highlights the need for practical guidance in transitioning from theory to practice, particularly in the context of real-world applications and job readiness [29][31]. - The article also mentions the availability of specialized small group courses to address specific industry needs and enhance practical skills [23][24].