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].