筹备了半年！端到端与VLA自动驾驶小班课来啦（一段式/两段式/扩散模型/VLA等）

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2: Challenges in Learning and Development - The rapid advancement in technology has made previous educational resources outdated, creating a need for updated learning paths that encompass multi-modal large models, BEV perception, reinforcement learning, and more [3][5].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].