端到端VLA这薪资，让我心动了。。。

Core Viewpoint - End-to-End Autonomous Driving (E2E) is identified as the core algorithm for intelligent driving mass production, marking a significant shift in the industry towards more integrated and efficient systems [2][4]. Group 1: Technology Overview - E2E can be categorized into single-stage and two-stage approaches, with the latter gaining traction following the recognition of UniAD at CVPR [2]. - The E2E system directly models the relationship between sensor inputs and vehicle control information, minimizing errors associated with modular approaches [2]. - The introduction of BEV perception has bridged gaps between modular methods, leading to a technological leap in the field [2]. Group 2: Challenges in Learning - The rapid development of E2E technology has made previous educational resources outdated, creating a need for updated learning materials [5]. - The fragmented nature of knowledge across various domains complicates the learning process for newcomers, often leading to abandonment before mastery [5]. - A lack of high-quality documentation in E2E research increases the difficulty of entry into the field [5]. Group 3: Course Development - A new course titled "End-to-End and VLA Autonomous Driving" has been developed to address the challenges faced by learners [6]. - The course aims to provide a quick entry into core technologies using accessible language and examples, facilitating easier expansion into specific knowledge areas [6]. - It focuses on building a framework for understanding E2E research and enhancing research capabilities by categorizing papers and extracting innovative points [7]. Group 4: Course Structure - The course is structured into several chapters, covering topics from the history and evolution of E2E algorithms to practical applications and advanced techniques [11][12][20]. - Key areas of focus include the introduction of E2E algorithms, background knowledge on relevant technologies, and detailed explorations of both single-stage and two-stage methods [11][12][20]. - Practical components are integrated into the curriculum to ensure a comprehensive understanding of theoretical concepts [8]. Group 5: Expected Outcomes - Participants are expected to achieve a level of proficiency equivalent to one year of experience as an E2E autonomous driving algorithm engineer [27]. - The course will cover a wide range of methodologies, including single-stage, two-stage, world models, and diffusion models, providing a holistic view of the E2E landscape [27]. - A deeper understanding of key technologies such as BEV perception, multimodal large models, and reinforcement learning will be developed [27].