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 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 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].