Core Viewpoint - The article emphasizes the importance of understanding end-to-end (E2E) algorithms and Visual Language Models (VLA) in the context of autonomous driving, highlighting the rapid development and complexity of the technology stack involved [2][32]. Summary by Sections Introduction to End-to-End and VLA - The article discusses the evolution of large language models over the past five years, indicating a significant technological advancement in the field [2]. Technical Foundations - The Transformer architecture is introduced as a fundamental component for understanding large models, with a focus on attention mechanisms and multi-head attention [8][12]. - Tokenization methods such as BPE (Byte Pair Encoding) and positional encoding are explained as essential for processing sequences in models [13][9]. Course Overview - A new course titled "End-to-End and VLA Autonomous Driving" is launched, aimed at providing a comprehensive understanding of the technology stack and practical applications in autonomous driving [21][33]. - The course is structured into five chapters, covering topics from basic E2E algorithms to advanced VLA methods, including practical assignments [36][48]. Key Learning Objectives - The course aims to equip participants with the ability to classify research papers, extract innovative points, and develop their own research frameworks [34]. - Emphasis is placed on the integration of theory and practice, ensuring that learners can apply their knowledge effectively [35]. Industry Demand and Career Opportunities - The demand for VLA/VLM algorithm experts is highlighted, with salary ranges between 40K to 70K for positions requiring 3-5 years of experience [29]. - The course is positioned as a pathway for individuals looking to transition into roles focused on autonomous driving algorithms, particularly in the context of emerging technologies [28].