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 - 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].

Core Insights - The article discusses the transition in intelligent driving technology from rule-driven to data-driven approaches, highlighting the emergence of VLA (Vision-Language Action) as a more straightforward and effective method compared to traditional end-to-end systems [1][2] - The challenges in the current VLA technology stack are emphasized, including the complexity and fragmentation of knowledge, which makes it difficult for newcomers to enter the field [2][3] - A new practical course on VLA has been developed to address these challenges, providing a structured learning path for students interested in advanced knowledge in autonomous driving [3][4][5] Summary by Sections Introduction to VLA - The article introduces VLA as a significant advancement in autonomous driving, offering a cleaner approach than traditional end-to-end systems, while also addressing corner cases more effectively [1] Challenges in Learning VLA - The article outlines the difficulties faced by learners in navigating the complex and fragmented knowledge landscape of VLA, which includes a plethora of algorithms and a lack of high-quality documentation [2] Course Development - A new course titled "Autonomous Driving VLA Practical Course" has been created to provide a comprehensive overview of the VLA technology stack, aiming to facilitate easier entry into the field for students [3][4] Course Features - The course is designed to address key pain points, offering quick entry into the subject matter through accessible language and examples [3] - It aims to build a framework for understanding VLA research and enhance research capabilities by teaching students how to categorize papers and extract innovative points [4] - The course includes practical components to ensure that theoretical knowledge is effectively applied in real-world scenarios [5] Course Outline - The course covers various topics, including the origins of VLA, foundational algorithms, and the differences between modular and integrated VLA systems [6][15][19][20] - It also includes practical coding exercises and projects to reinforce learning and application of concepts [22][24][26] Instructor Background - The course is led by experienced instructors with a strong background in multi-modal perception, autonomous driving, and large model frameworks, ensuring high-quality education [27] Learning Outcomes - Upon completion, students are expected to have a thorough understanding of current advancements in VLA, core algorithms, and the ability to apply their knowledge in practical settings [28][29]

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].

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, where noise follows a specific distribution. The model learns to recover original data from noise through a forward diffusion process and a reverse generation process [1][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. They can handle both continuous and discrete noise, making them versatile for various decision-making tasks [11]. Course Overview - The article promotes a new course titled "End-to-End and VLA Autonomous Driving," developed in collaboration with top algorithm experts. The course aims to provide in-depth knowledge of end-to-end algorithms and VLA technology [15][22]. Course Structure - The course is structured into several chapters, covering topics such as: - Comprehensive understanding of end-to-end autonomous driving [18] - In-depth background knowledge including large language models, BEV perception, and Diffusion Model theory [21][28] - Exploration of two-stage and one-stage end-to-end methods, including the latest advancements in the field [29][36] Learning Outcomes - Participants are expected to gain a solid understanding of the end-to-end technology framework, including one-stage, two-stage, world models, and Diffusion Models. The course also aims to enhance knowledge of key technologies like BEV perception and reinforcement learning [41][43].

Core Viewpoint - The article discusses the current development status of end-to-end autonomous driving algorithms, comparing them with traditional algorithms and highlighting their advantages and limitations [3][5][6]. Group 1: Traditional vs. End-to-End Algorithms - Traditional autonomous driving algorithms follow a pipeline of perception, prediction, and planning, where each module has distinct inputs and outputs [5][6]. - The perception module takes sensor data as input and outputs bounding boxes for the prediction module, which then outputs trajectories for the planning module [6]. - End-to-end algorithms, in contrast, take raw sensor data as input and directly output path points, simplifying the process and reducing error accumulation [6][10]. Group 2: Limitations of End-to-End Algorithms - End-to-end algorithms face challenges such as lack of interpretability, safety guarantees, and issues related to causal confusion [12][57]. - The reliance on imitation learning in end-to-end algorithms limits their ability to handle corner cases effectively, as they may misinterpret rare scenarios as noise [11][57]. - The inherent noise in ground truth data can lead to suboptimal learning outcomes, as human driving data may not represent the best possible actions [11][57]. Group 3: Current End-to-End Algorithm Implementations - The ST-P3 algorithm is highlighted as an early example of end-to-end autonomous driving, focusing on spatiotemporal learning with three core modules: perception, prediction, and planning [14][15]. - Innovations in ST-P3 include a perception module that uses a self-centered cumulative alignment technique, a dual-path prediction mechanism, and a planning module that incorporates prior information for trajectory optimization [15][19][20]. Group 4: Advanced Techniques in End-to-End Algorithms - The UniAD framework introduces a multi-task approach by incorporating five auxiliary tasks to enhance performance, addressing the limitations of traditional modular stacking methods [24][25]. - The system employs a full Transformer architecture for planning, integrating various interaction modules to improve trajectory prediction and planning accuracy [26][29]. - The VAD (Vectorized Autonomous Driving) method utilizes vectorized representations to better express structural information of map elements, enhancing computational speed and efficiency [32][33]. Group 5: Future Directions and Challenges - The article emphasizes the need for further research to overcome the limitations of current end-to-end algorithms, particularly in optimizing learning processes and handling exceptional cases [57]. - The introduction of multi-modal planning and multi-model learning approaches aims to improve trajectory prediction stability and performance [56][57].

Core Viewpoint - The article discusses the rapid evolution and challenges in the field of end-to-end autonomous driving technology, emphasizing the need for a comprehensive understanding of various algorithms and models to succeed in this competitive industry [2][4][6]. Group 1: Industry Trends - The shift from modular approaches to end-to-end systems in autonomous driving aims to eliminate cumulative errors between modules, marking a significant technological leap [2]. - The emergence of various algorithms and models, such as UniAD and BEV perception, indicates a growing focus on integrating multiple tasks into a unified framework [4][9]. - The demand for knowledge in multi-modal large models, reinforcement learning, and diffusion models is increasing, reflecting the industry's need for versatile skill sets [5][20]. Group 2: Learning Challenges - New entrants face difficulties due to the fragmented nature of knowledge and the overwhelming volume of research papers in the field, often leading to early abandonment of learning [5][6]. - The lack of high-quality documentation and practical guidance further complicates the transition from theory to practice in end-to-end autonomous driving research [5][6]. Group 3: Course Offerings - A new course titled "End-to-End and VLA Autonomous Driving" has been developed to address the learning challenges, focusing on practical applications and theoretical foundations [6][24]. - The course is structured to provide a comprehensive understanding of end-to-end algorithms, including their historical development and current trends [11][12]. - Practical components, such as real-world projects and assignments, are included to ensure that participants can apply their knowledge effectively [8][21]. Group 4: Course Content Overview - The course covers various topics, including the introduction to end-to-end algorithms, background knowledge on relevant technologies, and detailed explorations of both one-stage and two-stage end-to-end methods [11][12][13]. - Specific chapters focus on advanced topics like world models and diffusion models, which are crucial for understanding the latest advancements in autonomous driving [15][17][20]. - The final project involves practical applications of reinforcement learning from human feedback (RLHF), allowing participants to gain hands-on experience [21].

Core Viewpoint - The article highlights the launch of the Li Auto i8, which features significant upgrades in its driver assistance capabilities, particularly through the integration of the VLA (Vision-Language-Action) model, marking a milestone in the mass production of autonomous driving technology [2][3]. Summary by Sections VLA Model Capabilities - The VLA model enhances understanding of semantics through multimodal input, improves reasoning with a thinking chain, and aligns more closely with human driving intuition. Its four core capabilities include spatial understanding, reasoning ability, communication and memory, and behavioral ability [3][6]. Industry Development - The VLA represents a new milestone in the mass production of autonomous driving, with many companies investing in human resources for research and development. The transition from E2E (End-to-End) and VLM (Vision-Language Model) to VLA indicates a progressive technological evolution [5][8]. Educational Initiatives - In response to the growing interest in transitioning to VLA-related roles, the industry has launched a specialized course titled "End-to-End and VLA Autonomous Driving Small Class," aimed at providing in-depth knowledge of the algorithms and technical development in this field [7][15]. Course Structure and Content - The course covers various aspects of end-to-end algorithms, including historical development, background knowledge, and specific methodologies such as two-stage and one-stage end-to-end approaches. It emphasizes practical applications and theoretical foundations [21][22][23][24]. Job Market Insights - The demand for VLA/VLM algorithm experts is high, with salary ranges for positions varying based on experience and educational background. For instance, positions for VLA/VLM algorithm engineers typically offer salaries between 35K to 70K for those with 3-5 years of experience [11]. Learning Outcomes - Participants in the course are expected to achieve a level of understanding equivalent to that of an autonomous driving algorithm engineer with one year of experience, covering key technologies such as BEV perception, multimodal models, and reinforcement learning [32].

Core Viewpoint - End-to-end (E2E) autonomous driving is currently the core algorithm for mass production in intelligent driving, with significant advancements in the VLA (Vision-Language Architecture) and VLM (Vision-Language Model) systems, leading to high demand for related positions in the industry [2][4]. Summary by Sections Section 1: Background Knowledge - The course aims to provide a comprehensive understanding of end-to-end autonomous driving, including its historical development and the transition from modular to end-to-end approaches [21]. - Key technical stacks such as VLA, diffusion models, and reinforcement learning are essential for understanding the current landscape of autonomous driving technology [22]. Section 2: Job Market Insights - Positions related to VLA/VLM algorithms offer lucrative salaries, with 3-5 years of experience earning between 40K to 70K monthly, and top talents in the field can earn up to 1 million annually [10]. - The demand for VLA-related roles is increasing, indicating a shift in the industry towards advanced model architectures [9]. Section 3: Course Structure - The course is structured into five chapters, covering topics from basic concepts of end-to-end algorithms to advanced applications in VLA and reinforcement learning [19][30]. - Practical components are included to bridge the gap between theory and application, ensuring participants can implement learned concepts in real-world scenarios [18]. Section 4: Technical Innovations - Various approaches within end-to-end frameworks are explored, including two-stage and one-stage methods, with notable models like PLUTO and UniAD leading the way [4][23]. - The introduction of diffusion models has revolutionized trajectory prediction, allowing for better adaptability in uncertain driving environments [24]. Section 5: Learning Outcomes - Participants are expected to achieve a level of proficiency equivalent to one year of experience as an end-to-end autonomous driving algorithm engineer, mastering key technologies and frameworks [32]. - The course emphasizes the importance of understanding BEV perception, multimodal models, and reinforcement learning to stay competitive in the evolving job market [32].

Core Viewpoint - End-to-End Autonomous Driving is a key algorithm for intelligent driving mass production, with significant salary potential for related positions, and it has evolved into various technical directions since the introduction of UniAD [2][4]. Group 1: Technical Directions - End-to-End Autonomous Driving can be categorized into one-stage and two-stage approaches, with various subfields emerging under each category [2][4]. - The core advantage of end-to-end systems is the direct modeling from sensor input to vehicle planning/control information, avoiding error accumulation seen in modular methods [2]. - Notable algorithms include PLUTO for two-stage end-to-end, UniAD for perception-based one-stage, OccWorld for world model-based one-stage, and DiffusionDrive for diffusion model-based one-stage [4]. Group 2: Industry Trends - The demand for VLA/VLM algorithm experts is increasing, with salary ranges for positions requiring 3-5 years of experience being between 40K-70K [9]. - The industry is witnessing a shift towards large model algorithms, with companies focusing on VLA as the next generation of autonomous driving solutions [8][9]. Group 3: Course Offerings - A new course titled "End-to-End and VLA Autonomous Driving" is being offered to help individuals understand the complexities of end-to-end algorithms and their applications [15][28]. - The course covers various topics, including background knowledge, two-stage end-to-end, one-stage end-to-end, and practical applications of reinforcement learning [20][22][24]. - The course aims to provide a comprehensive understanding of the end-to-end framework, including key technologies like BEV perception, multi-modal large models, and diffusion models [31].