端到端作为当前自动驾驶量产的核心算法，所涉及的技术栈十分丰富。很多研究生的同学和转行的工业界小伙伴在刚开始接触时，往往会遇到很多问 题。目前业内主要有两大类范式：一段式和两段式。一段式最具代表性的就是UniAD，直接从传感器输入（视觉/Lidar/Radar等）建模自车轨迹的输出， 二段式基于感知结果进一步输出自车和他车的轨迹。 一段式端到端又可以进一步延伸出基于感知的一段式、基于扩散模型的一段式、基于世界模型的一段式以及基于VLA的一段式端到端算法。不难看出， 端到端已经衍生出很多子领域，尤其是基于VLA的相关算法，这两年相关论文在爆发式发表，工业界也在争先量产。 从模块化的量产算法发展到端到端，再到如今的VLA。核心算法涉及BEV感知、视觉语言模型VLM、扩散模型、强化学习、世界模型等等。通过学习端 到端与VLA自动驾驶，可以掌握学术界和工业界最前沿的技术方向。 最近几个月，我们收到了很多同学的咨询如何快速高效的入门端到端和VLA。所以我们联合了 工业界 和 学术界 的大佬开展了 《端到端与VLA自动驾 驶小班课》 和 《自动驾驶VLA和大模型实战课程》 ！ 扫码报名！抢占课程名额 课程大纲 自动驾驶VL ...

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 ...

Core Viewpoint - End-to-End Autonomous Driving (E2E) is identified as the core algorithm for intelligent driving mass production, with significant advancements and competition emerging in the industry following the recognition of UniAD at CVPR [2][3] Group 1: E2E Autonomous Driving Overview - E2E systems directly model the relationship between sensor inputs and vehicle control information, avoiding error accumulation seen in traditional modular approaches [2] - The introduction of BEV perception has bridged gaps between modular methods, leading to a significant technological leap [2] - The emergence of various algorithms indicates that UniAD is not the ultimate solution for E2E, highlighting the rapid development in this field [2] Group 2: Learning Challenges in E2E - The fast-paced development in E2E technology has made previous educational resources inadequate, necessitating a comprehensive understanding of multiple domains such as multimodal large models, BEV perception, and reinforcement learning [3][4] - Beginners face challenges due to fragmented knowledge and the overwhelming volume of literature, often leading to abandonment before mastering the concepts [3] Group 3: Course Development - A new course titled "End-to-End and VLA Autonomous Driving" has been developed to address learning challenges, focusing on practical and theoretical integration [4][5][6] - The course aims to provide a structured framework for understanding E2E research and enhance research capabilities by categorizing papers and extracting innovative points [5] Group 4: Course Structure - The course includes five chapters covering topics from the introduction of E2E algorithms to practical applications involving RLHF fine-tuning [9][10][11][12][13] - Key areas of focus include the evolution of E2E paradigms, the significance of VLA in the current landscape, and practical implementations of diffusion models [11][12] Group 5: Expected Outcomes - Participants are expected to achieve a level equivalent to one year of experience as an E2E autonomous driving algorithm engineer, mastering various methodologies and key technologies [18] - The course aims to facilitate the application of learned concepts in real-world projects, enhancing employability in the autonomous driving sector [18]

Core Viewpoint - End-to-End Autonomous Driving is the core algorithm for the next generation of intelligent driving mass production, marking a significant shift in the industry towards more integrated and efficient systems [1][3]. Group 1: End-to-End Autonomous Driving Overview - End-to-End Autonomous Driving can be categorized into single-stage and two-stage approaches, with the former directly modeling vehicle planning and control from sensor data, thus avoiding error accumulation seen in modular methods [1][4]. - The emergence of UniAD has initiated a new wave of competition in the autonomous driving sector, with various algorithms rapidly developing in response to its success [1][3]. Group 2: Challenges in Learning and Development - The rapid advancement in technology has made previous educational resources outdated, creating a need for updated learning paths that encompass multi-modal large models, BEV perception, reinforcement learning, and more [3][5]. - Beginners face significant challenges due to the fragmented nature of knowledge across various fields, making it difficult to extract frameworks and understand development trends [3][6]. Group 3: Course Structure and Content - The course on End-to-End and VLA Autonomous Driving aims to address these challenges by providing a structured learning path that includes practical applications and theoretical foundations [5][7]. - The curriculum covers the history and evolution of End-to-End algorithms, background knowledge necessary for understanding current technologies, and practical applications of various models [8][9]. Group 4: Key Technologies and Innovations - The course highlights significant advancements in two-stage and single-stage End-to-End methods, including notable algorithms like PLUTO and DiffusionDrive, which represent the forefront of research in the field [4][10][12]. - The integration of large language models (VLA) into End-to-End systems is emphasized as a critical area of development, with companies actively exploring new generation mass production solutions [13][14]. Group 5: Expected Outcomes and Skills Development - Upon completion of the course, participants are expected to reach a level equivalent to one year of experience as an End-to-End Autonomous Driving algorithm engineer, mastering various methodologies and key technologies [22][23]. - The course aims to equip participants with the ability to apply learned concepts to real-world projects, enhancing their employability in the autonomous driving sector [22][23].