Core Insights - The consensus among industry experts indicates that 2026 will be a pivotal year for the development of end-to-end (E2E) and VLA (Vision-Language Alignment) technologies in autonomous driving, with a focus on optimizing production processes rather than making significant algorithmic changes [1] - The industry is actively recruiting experienced algorithm engineers and developing talent to tackle the complex challenges ahead, particularly in areas such as BEV perception, large models, diffusion models, and reinforcement learning [1] Course Overview - The course on E2E and VLA autonomous driving is designed to provide a comprehensive learning path from principles to practical applications, developed in collaboration with industry leaders [3] - The course covers various aspects of E2E algorithms, including their historical development, advantages and disadvantages of different paradigms, and current trends in both academia and industry [6][7] - Key technical keywords that are expected to be frequently encountered in job interviews over the next two years are emphasized in the course content [7] Course Structure - Chapter 1 introduces the concept of E2E algorithms, discussing their evolution from modular approaches to current paradigms like VLA [6] - Chapter 2 focuses on the background knowledge necessary for understanding E2E technologies, including VLA, large language models, diffusion models, and reinforcement learning [11] - Chapter 3 delves into two-stage E2E algorithms, exploring their emergence and comparing them with one-stage approaches [7] - Chapter 4 presents one-stage E2E algorithms and VLA, highlighting various subfields and their contributions to achieving the ultimate goals of E2E systems [8] - Chapter 5 involves a practical assignment on RLHF (Reinforcement Learning from Human Feedback) fine-tuning, demonstrating how to build and experiment with pre-training and reinforcement learning modules [9] Learning Outcomes - The course aims to elevate participants to the level of an E2E autonomous driving algorithm engineer within approximately one year, covering a wide range of methodologies including one-stage, two-stage, world models, and diffusion models [15] - Participants will gain a deeper understanding of key technologies such as BEV perception, multimodal large models, reinforcement learning, and diffusion models, enabling them to apply their knowledge in real-world projects [15]

Core Viewpoint - The article discusses an advanced course on end-to-end (E2E) autonomous driving, focusing on the latest technologies such as BEV perception, Visual Language Models (VLM), diffusion models, and reinforcement learning, aimed at equipping participants with cutting-edge skills in the field [1][4][8]. Group 1: Course Structure - The course is divided into several chapters, starting with an introduction to end-to-end algorithms, covering the historical development and advantages of E2E methods over modular approaches [4]. - The second chapter focuses on background knowledge essential for understanding E2E technologies, including VLA, diffusion models, and reinforcement learning, which are crucial for job interviews in the next two years [5][9]. - The third chapter delves into two-stage E2E methods, discussing their emergence, advantages, and notable algorithms like PLUTO and CarPlanner [5][6]. - The fourth chapter highlights one-stage E2E methods and VLA, exploring various subfields and their contributions to achieving the ultimate goals of E2E systems [6][10]. Group 2: Practical Application - The course includes a major project on RLHF fine-tuning, allowing participants to apply their knowledge in practical scenarios, including building pre-training and reinforcement learning modules [7]. - The course aims to help participants reach a level equivalent to one year of experience as an E2E autonomous driving algorithm engineer, covering various methodologies and key technologies [13]. Group 3: Target Audience and Requirements - The course is designed for individuals with a foundational understanding of autonomous driving, familiar with basic modules, and concepts like transformer models, reinforcement learning, and BEV perception [11]. - Participants are expected to have a background in probability theory and linear algebra, as well as proficiency in Python and PyTorch [11].

Core Viewpoint - The article discusses the distinction between world models and end-to-end models in autonomous driving, clarifying that world models are not end-to-end but serve as a pathway to achieve end-to-end autonomous driving [2][3][4]. Group 1: Definitions and Concepts - End-to-end autonomous driving is defined as a model that processes information input on one end and outputs decision results without explicit information processing and decision logic [3]. - World models are defined as models that accept information input and internally establish a complete understanding of the environment, capable of reconstructing and predicting future changes [4]. Group 2: Course Introduction - A new course on world models has been launched, focusing on general world models, video generation, and OCC generation algorithms, including applications from Tesla and the Li Fei Fei team [5]. - The course aims to enhance understanding of end-to-end autonomous driving and is designed for individuals looking to enter the autonomous driving industry [15]. Group 3: Course Structure - Chapter 1 introduces world models and their relationship with end-to-end autonomous driving, covering historical development and current applications [10]. - Chapter 2 provides foundational knowledge on world models, including scene representation and relevant technologies like Transformer and BEV perception [10][16]. - Chapter 3 discusses general world models and popular algorithms such as Marble and Genie 3, explaining their core technologies and design philosophies [11]. - Chapter 4 focuses on video generation world models, detailing significant works and advancements in this area [12]. - Chapter 5 covers OCC generation models, discussing their applications and potential for trajectory planning [13]. - Chapter 6 shares industry insights and interview preparation tips for roles related to world models [14]. Group 4: Learning Outcomes - The course aims to elevate participants to the level of a world model autonomous driving algorithm engineer within approximately one year, covering key technologies and enabling practical application in projects [18].

Core Viewpoint - The article emphasizes the growing demand for technical talent in the autonomous driving sector, particularly in end-to-end and VLA (Vision-Language-Action) technologies, with companies willing to invest significantly in experienced professionals, starting salaries reaching millions annually [2]. Course Offerings - The article outlines several specialized courses aimed at enhancing skills in autonomous driving, including "End-to-End Practical Class for Mass Production," "End-to-End and VLA Autonomous Driving Class," and "VLA and Large Model Practical Course," catering to various levels from beginners to advanced professionals [4][7][12]. End-to-End Mass Production Course - This course focuses on the practical implementation of end-to-end autonomous driving, covering key modules such as navigation information application, reinforcement learning optimization, diffusion and autoregressive production experience, and spatiotemporal joint planning [4]. End-to-End and VLA Autonomous Driving Course - This course addresses macro aspects of end-to-end autonomous driving, detailing key algorithms and theoretical foundations, including BEV perception, large language models, diffusion models, and reinforcement learning [7]. VLA and Large Model Practical Course - This course requires participants to have a GPU with recommended computing power of 4090 or higher, a foundational understanding of autonomous driving, and familiarity with concepts like transformer models and reinforcement learning [11]. Instructor Profiles - The courses are led by industry experts with strong academic backgrounds, including those with multiple published papers in top conferences and extensive experience in algorithm development and mass production in autonomous driving [6][9][14][15].

Core Viewpoint - The article emphasizes the growing demand for skills in end-to-end and VLA (Vision-Language-Action) autonomous driving, highlighting the saturation of job opportunities in these areas and the urgency for newcomers to acquire relevant knowledge and skills quickly [1]. Course Offerings - The "End-to-End and VLA Autonomous Driving Course" is designed to provide comprehensive training in VLA, covering topics from VLM as an autonomous driving interpreter to modular and integrated VLA, and current mainstream inference-enhanced VLA [1]. - The "Autonomous Driving VLA and Large Model Practical Course" focuses on foundational theories and practical applications, including Vision/Language/Action modules, reinforcement learning, and diffusion models, with a special section on building VLA models and datasets from scratch [1]. Instructor Team - The course is led by experts from both academia and industry, including individuals with extensive research and practical experience in multimodal perception, autonomous driving VLA, and large model frameworks [6][8][11]. Target Audience - The courses are aimed at individuals with a foundational understanding of autonomous driving, familiarity with key technologies such as transformer models and reinforcement learning, and a basic knowledge of probability and linear algebra [12][13].

Core Viewpoint - 2023 marks the year of end-to-end production, with 2024 expected to be a significant year for end-to-end production in the automotive industry, as leading new forces and manufacturers have already achieved end-to-end production [1][3]. Group 1: End-to-End Production Development - The automotive industry is witnessing rapid development in end-to-end methods, particularly the one-stage approach exemplified by UniAD, which directly models vehicle trajectories from sensor inputs [1][3]. - There are two main paradigms in the industry: one-stage and two-stage methods, with the one-stage approach gaining traction and leading to various derivatives based on perception, world models, diffusion models, and VLA [3][5]. Group 2: Course Overview - A course titled "End-to-End and VLA Autonomous Driving" has been launched, focusing on cutting-edge algorithms in both one-stage and two-stage end-to-end methods, aimed at bridging academic and industrial advancements [5][15]. - The course is structured into several chapters, covering the history and evolution of end-to-end methods, background knowledge on VLA, and detailed discussions on both one-stage and two-stage approaches [9][10][12]. Group 3: Key Technologies - The course emphasizes critical technologies such as BEV perception, visual language models (VLM), diffusion models, and reinforcement learning, which are essential for mastering the latest advancements in autonomous driving [5][11][19]. - The second chapter of the course is highlighted as containing the most frequently asked technical keywords for job interviews in the next two years [10]. Group 4: Practical Applications - The course includes practical assignments, such as RLHF fine-tuning, allowing participants to apply their knowledge in real-world scenarios and understand how to build and experiment with pre-trained and reinforcement learning modules [13][19]. - The curriculum also covers various subfields of one-stage end-to-end methods, including those based on perception, world models, diffusion models, and VLA, providing a comprehensive understanding of the current landscape in autonomous driving technology [14][19].

Core Insights - The article discusses the evolution of end-to-end algorithms in autonomous driving, highlighting the transition from modular production algorithms to end-to-end and now to Vision-Language Alignment (VLA) models [1][3] - It emphasizes the rich technology stack involved in end-to-end algorithms, including BEV perception, visual language models (VLM), diffusion models, reinforcement learning, and world models [3] Summary by Sections End-to-End Algorithms - End-to-end algorithms are categorized into two main paradigms: single-stage and two-stage, with UniAD being a representative of the single-stage approach [1] - Single-stage can further branch into various subfields, particularly those based on VLA, which have seen a surge in related publications and industrial applications in recent years [1] Courses Offered - The article promotes two courses: "End-to-End and VLA Autonomous Driving Small Class" and "Practical Course on Autonomous Driving VLA and Large Models," aimed at helping individuals quickly and efficiently enter the field [3] - The "Practical Course" focuses on VLA, covering topics from VLM as an autonomous driving interpreter to modular and integrated VLA, along with detailed theoretical foundations [3][12] Instructor Team - The instructor team includes experts from both academia and industry, with backgrounds in multi-modal perception, autonomous driving VLA, and large model frameworks [8][11][14] - Notable instructors have published numerous papers in top-tier conferences and have extensive experience in research and practical applications in autonomous driving and large models [8][11][14] Target Audience - The courses are designed for individuals with a foundational understanding of autonomous driving, familiar with basic modules, and have knowledge of transformer models, reinforcement learning, and BEV perception [15][17]

Core Viewpoint - 2023 marks the year of end-to-end production, with 2024 expected to be a significant year for end-to-end production in the automotive industry, as leading new forces and manufacturers have already achieved end-to-end production [1][3]. Group 1: End-to-End Production Development - The automotive industry is witnessing rapid development in end-to-end production, particularly in one-stage and two-stage paradigms, with one-stage methods like UniAD being prominent [1][3]. - Various one-stage methods have emerged, including perception-based, world model-based, diffusion model-based, and VLA-based approaches, indicating a strong push from both autonomous driving companies and vehicle manufacturers towards self-research and mass production of end-to-end autonomous driving [3][5]. Group 2: Course Overview - A course titled "End-to-End and VLA Autonomous Driving" has been launched, focusing on cutting-edge algorithms in both one-stage and two-stage end-to-end methods, aimed at bridging academic and industrial advancements [5][15]. - The course is structured into several chapters, covering topics such as the history and evolution of end-to-end algorithms, background knowledge on VLA, and detailed discussions on two-stage and one-stage end-to-end methods [9][10][12]. Group 3: Key Technologies and Techniques - The course emphasizes key technologies such as BEV perception, visual language models (VLM), diffusion models, and reinforcement learning, which are essential for mastering the latest advancements in autonomous driving [5][11]. - The second chapter of the course is highlighted as crucial for understanding the most frequently asked technical keywords in job interviews over the next two years [10]. Group 4: Practical Applications and Outcomes - The course includes practical assignments, such as RLHF fine-tuning, allowing participants to apply their knowledge in real-world scenarios and understand how to build and experiment with reinforcement learning modules [13][19]. - By completing the course, participants are expected to reach a level equivalent to one year of experience as an end-to-end autonomous driving algorithm engineer, gaining a comprehensive understanding of various methodologies and their applications [19].

Core Insights - The article discusses the evolution of end-to-end algorithms in autonomous driving, highlighting the transition from modular production algorithms to end-to-end and now to Vision-Language Alignment (VLA) models [1][3] - It emphasizes the rich technology stack involved in end-to-end algorithms, including BEV perception, visual language models (VLM), diffusion models, reinforcement learning, and world models [3][10] Summary by Sections End-to-End Algorithms - End-to-end algorithms are categorized into two main paradigms: single-stage and two-stage, with UniAD being a representative of the single-stage approach [1] - Single-stage can further branch into various subfields, particularly those based on VLA, which have seen a surge in related publications and industrial applications in recent years [1] VLA and Course Offerings - The article mentions the launch of courses aimed at helping individuals quickly and efficiently learn about end-to-end and VLA in autonomous driving, featuring collaboration between industry and academia [3] - The "VLA and Large Model Practical Course" focuses on VLA, covering topics from VLM as an autonomous driving interpreter to modular and integrated VLA approaches [3] Course Structure and Faculty - The course structure includes a comprehensive overview of VLA, with detailed theoretical foundations in Vision, Language, and Action, as well as practical assignments to build VLA models and datasets from scratch [3][10] - The teaching team consists of experienced professionals from top academic institutions and industry, with backgrounds in multimodal perception, autonomous driving, and large model frameworks [7][9][10] Target Audience and Requirements - The courses are designed for individuals with a foundational understanding of autonomous driving and familiarity with key technologies such as transformer models, reinforcement learning, and BEV perception [13] - Participants are expected to have a basic knowledge of probability theory, linear algebra, and programming skills in Python and PyTorch [13]

Core Viewpoint - The article discusses the evolution of end-to-end (E2E) autonomous driving technology, emphasizing the transition from rule-based to data-driven approaches, and highlights the limitations of current models in handling complex scenarios. It introduces Visual Language Models (VLM) and Visual Language Agents (VLA) as potential solutions to enhance the capabilities of autonomous driving systems [2][3]. Summary by Sections Introduction to VLA - VLA represents a shift from merely imitating human behavior to understanding and interacting with the physical world, addressing the limitations of traditional E2E models in complex driving scenarios [2]. Challenges in Autonomous Driving - The VLA technology stack is still evolving, with numerous algorithms emerging, indicating a lack of convergence in the field [3]. Course Overview - A course titled "Autonomous Driving VLA and Large Model Practical Course" is being prepared to address various aspects of VLA, including its origins, algorithms, and practical applications [5]. Learning Objectives - The course aims to provide a comprehensive understanding of VLA, covering topics such as data set creation, model training, and performance enhancement [5][17]. Course Structure - The course is structured into several chapters, each focusing on different aspects of VLA, including algorithm introduction, foundational knowledge, VLM as an interpreter, modular and integrated VLA, reasoning enhancement, and practical assignments [20][26][31][34][36]. Instructor Background - The instructors have extensive experience in multimodal perception, autonomous driving, and large model frameworks, contributing to the course's credibility [38]. Expected Outcomes - Participants are expected to gain a thorough understanding of current advancements in VLA, master core algorithms, and be able to apply their knowledge in practical settings [39][40]. Course Schedule - The course is set to begin on October 20, with a structured timeline for each chapter's release [43].