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 ...

Core Viewpoint - 2023 is identified as the year of end-to-end production, with 2024 expected to be a significant year for this development in the automotive industry, particularly in autonomous driving technology [1][3]. Group 1: End-to-End Production - Leading new forces and manufacturers have already achieved end-to-end production [1]. - There are two main paradigms in the industry: one-stage and two-stage approaches, with UniAD being a representative of the one-stage method [1]. Group 2: Development Trends - Since last year, the one-stage end-to-end approach has rapidly evolved, leading to various derivatives such as perception-based, world model-based, diffusion model-based, and VLA-based one-stage methods [3]. - Major autonomous driving companies are focusing on self-research and mass production of end-to-end autonomous driving solutions [3]. Group 3: Course Offerings - A course titled "End-to-End and VLA Autonomous Driving" has been launched, covering cutting-edge algorithms in both one-stage and two-stage end-to-end approaches [5]. - The course aims to provide insights into the latest technologies in the field, including BEV perception, visual language models, diffusion models, and reinforcement learning [5]. Group 4: Course Structure - The course consists of several chapters, starting with an introduction to end-to-end algorithms, followed by background knowledge essential for understanding the technology stack [9][10]. - The second chapter focuses on the most frequently asked technical keywords in job interviews over the next two years [10]. - Subsequent chapters delve into two-stage end-to-end methods, one-stage end-to-end methods, and practical assignments involving RLHF fine-tuning [12][13]. Group 5: Learning Outcomes - Upon completion, participants are expected to reach a level equivalent to one year of experience as an end-to-end autonomous driving algorithm engineer [19]. - The course aims to deepen understanding of key technologies such as BEV perception, multimodal large models, and reinforcement learning, enabling participants to apply learned concepts to real projects [19].

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].

Core Viewpoint - The company has launched a "Super Discount Card" to address feedback regarding high course prices, offering a 30% discount on all courses for a year [2][4]. Group 1: Course Offerings - The company has introduced several new courses in the field of autonomous driving, including "End-to-End and VLA Autonomous Driving Small Class," "End-to-End and Planning Control (Third Session)," and "4D Annotation Algorithm Employment Small Class" [2]. - The "End-to-End and VLA" course has received positive feedback from participants, indicating strong interest and satisfaction [2]. Group 2: Discount Card Details - The "Super Discount Card" is priced at 299 yuan and provides a 30% discount on all courses related to autonomous driving and embodied intelligence, including future courses [4]. - The card is valid for one year from the date of purchase and can be fully refunded if no courses are purchased within that year [4]. - The promotional period for purchasing the discount card is from September 1 to September 14 [4].

Core Viewpoint - The company has launched a "Super Discount Card" to address feedback regarding high course prices in the field of autonomous driving, offering a 30% discount on all courses for a limited time [2][4]. Group 1: Course Offerings - The company has introduced several new courses in autonomous driving, including "End-to-End and VLA Autonomous Driving Small Class," "End-to-End and Planning Control (Third Session)," and "4D Annotation Algorithm Employment Small Class," which have received positive feedback [2]. - Future plans include launching additional courses focused on VLA and model deployment [2]. Group 2: Discount Card Details - The "Super Discount Card" is priced at 299 yuan and provides a 30% discount on all courses related to autonomous driving and embodied intelligence self-research courses, including future new courses [4]. - The card is valid for one year from the date of purchase and is available for a limited time from September 1 to September 14 [4]. - A full refund is available if no courses are purchased within one year of buying the discount card [4].

Core Viewpoint - The article emphasizes the importance of understanding end-to-end (E2E) algorithms and Visual Language Models (VLA) in the context of autonomous driving, highlighting the rapid development and complexity of the technology stack involved [2][32]. Summary by Sections Introduction to End-to-End and VLA - The article discusses the evolution of large language models over the past five years, indicating a significant technological advancement in the field [2]. Technical Foundations - The Transformer architecture is introduced as a fundamental component for understanding large models, with a focus on attention mechanisms and multi-head attention [8][12]. - Tokenization methods such as BPE (Byte Pair Encoding) and positional encoding are explained as essential for processing sequences in models [13][9]. Course Overview - A new course titled "End-to-End and VLA Autonomous Driving" is launched, aimed at providing a comprehensive understanding of the technology stack and practical applications in autonomous driving [21][33]. - The course is structured into five chapters, covering topics from basic E2E algorithms to advanced VLA methods, including practical assignments [36][48]. Key Learning Objectives - The course aims to equip participants with the ability to classify research papers, extract innovative points, and develop their own research frameworks [34]. - Emphasis is placed on the integration of theory and practice, ensuring that learners can apply their knowledge effectively [35]. Industry Demand and Career Opportunities - The demand for VLA/VLM algorithm experts is highlighted, with salary ranges between 40K to 70K for positions requiring 3-5 years of experience [29]. - The course is positioned as a pathway for individuals looking to transition into roles focused on autonomous driving algorithms, particularly in the context of emerging technologies [28].

Core Viewpoint - The article discusses the development and significance of end-to-end (E2E) algorithms in autonomous driving, emphasizing the integration of various advanced technologies such as large language models (LLMs), diffusion models, and reinforcement learning (RL) in enhancing the capabilities of autonomous systems [21][31]. Summary by Sections Section 1: Overview of End-to-End Autonomous Driving - The first chapter provides a comprehensive overview of the evolution of end-to-end algorithms, explaining the transition from modular approaches to end-to-end solutions, and discussing the advantages and challenges of different paradigms [40]. Section 2: Background Knowledge - The second chapter focuses on the technical stack associated with end-to-end systems, detailing the importance of LLMs, diffusion models, and reinforcement learning, which are crucial for understanding the future job market in this field [41][42]. Section 3: Two-Stage End-to-End Systems - The third chapter delves into two-stage end-to-end systems, exploring their emergence, advantages, and disadvantages, while also reviewing notable works in the field such as PLUTO and CarPlanner [42][43]. Section 4: One-Stage End-to-End and VLA - The fourth chapter highlights one-stage end-to-end systems, discussing various subfields including perception-based methods and the latest advancements in VLA (Vision-Language Alignment), which are pivotal for achieving the ultimate goals of autonomous driving [44][50]. Section 5: Practical Application and RLHF Fine-Tuning - The fifth chapter includes a major project focused on RLHF (Reinforcement Learning from Human Feedback) fine-tuning, providing practical insights into building pre-training and reinforcement learning modules, which are applicable to VLA-related algorithms [52]. Course Structure and Learning Outcomes - The course aims to equip participants with a solid understanding of end-to-end autonomous driving technologies, covering essential frameworks and methodologies, and preparing them for roles in the industry [56][57].

Core Viewpoint - The article emphasizes the significance of VLA (Vision-Language Alignment) as a new milestone in the mass production of autonomous driving technology, highlighting the progressive development from E2E (End-to-End) to VLA, and the growing interest from professionals in transitioning to this field [1][11]. Course Overview - The course titled "End-to-End and VLA Autonomous Driving Small Class" aims to provide in-depth knowledge of E2E and VLA algorithms, addressing the challenges faced by individuals looking to transition into this area [1][12]. - The curriculum is designed to cover various aspects of autonomous driving technology, including foundational knowledge, advanced models, and practical applications [5][15]. Course Structure - **Chapter 1**: Introduction to End-to-End Algorithms, covering the historical development and the transition from modular to end-to-end approaches, including the advantages and challenges of each paradigm [17]. - **Chapter 2**: Background knowledge on E2E technology stacks, focusing on key areas such as VLA, diffusion models, and reinforcement learning, which are crucial for future job interviews [18]. - **Chapter 3**: Exploration of two-stage end-to-end methods, discussing notable algorithms and their advantages compared to one-stage methods [18]. - **Chapter 4**: In-depth analysis of one-stage end-to-end methods, including various subfields like perception-based and world model-based approaches, culminating in the latest VLA techniques [19]. - **Chapter 5**: Practical assignment focusing on RLHF (Reinforcement Learning from Human Feedback) fine-tuning, providing hands-on experience with pre-training and reinforcement learning modules [21]. Target Audience and Learning Outcomes - The course is aimed at individuals with a foundational understanding of autonomous driving and related technologies, such as transformer models and reinforcement learning [28]. - Upon completion, participants are expected to achieve a level equivalent to one year of experience as an end-to-end autonomous driving algorithm engineer, mastering various methodologies and being able to apply learned concepts to real-world projects [28].

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 (E2E) autonomous driving is currently the core algorithm for mass production in intelligent driving, with significant advancements in the VLM/VLA systems leading to high demand for related positions and salaries reaching up to 1 million annually [2][11]. Group 1: Industry Trends - The concept of E2E has evolved significantly, with various technical schools emerging, yet many still struggle to understand its workings and distinctions between single-stage and two-stage approaches [2][4]. - The introduction of VLA (Vision-Language Architecture) is seen as a new frontier in autonomous driving, with companies actively researching and developing new generation mass production solutions [21][22]. Group 2: Educational Initiatives - A new course titled "End-to-End and VLA Autonomous Driving" has been launched to address the challenges faced by newcomers in the field, focusing on practical applications and theoretical foundations [14][27]. - The course aims to provide a comprehensive understanding of E2E autonomous driving, covering various models and methodologies, including diffusion models and reinforcement learning [6][19][21]. Group 3: Job Market Insights - The job market for VLA/VLM algorithm experts is robust, with salaries for positions requiring 3-5 years of experience ranging from 40K to 70K monthly, indicating a strong demand for skilled professionals [11][12]. - Positions such as VLA model quantization deployment engineers and multi-modal VLA model direction experts are particularly sought after, reflecting the industry's shift towards advanced algorithmic solutions [11][12].