从端到端到VLA，自动驾驶量产开始往这个方向发展...

Core Viewpoint - End-to-end (E2E) autonomous driving is currently the core algorithm for mass production in the intelligent driving sector, with significant advancements in VLM (Vision-Language Model) and VLA (Vision-Language Architecture) systems driving the industry forward [2][3]. Group 1: Industry Trends - The E2E approach has become a competitive focus for domestic new energy vehicle manufacturers, with the emergence of VLA concepts leading to a new wave of production scheme iterations [2]. - Salaries for positions related to VLM/VLA are reported to reach up to one million annually, with monthly salaries around 70K [2]. - The rapid development of technology has made previous solutions inadequate, necessitating a comprehensive understanding of various technical fields such as multimodal large models, BEV perception, reinforcement learning, and diffusion models [3][4]. Group 2: Educational Initiatives - A new course titled "End-to-End and VLA Autonomous Driving" has been developed to address the challenges faced by learners in this complex field, focusing on practical applications and theoretical foundations [4][5][6]. - The course aims to provide a structured learning path, helping students build a framework for research and enhance their research capabilities by categorizing papers and extracting innovative points [5]. - Practical components are included to ensure a complete learning loop from theory to application, addressing the gap between academic knowledge and real-world implementation [6]. Group 3: Course Structure - The course is divided into several chapters, covering topics such as the history and evolution of E2E algorithms, background knowledge on relevant technologies, and detailed explorations of both one-stage and two-stage E2E methods [9][10][11]. - Key areas of focus include the introduction of various E2E paradigms, the significance of world models, and the application of diffusion models in trajectory prediction [11][12]. - The final chapter includes a major project on RLHF (Reinforcement Learning from Human Feedback) fine-tuning, allowing students to apply their knowledge in practical scenarios [13]. Group 4: Target Audience and Outcomes - The course is designed for individuals with a foundational understanding of autonomous driving and related technologies, aiming to elevate their expertise to a level comparable to that of an E2E autonomous driving algorithm engineer within a year [20]. - Participants will gain a comprehensive understanding of E2E frameworks, including one-stage, two-stage, world models, and diffusion models, as well as deeper insights into key technologies like BEV perception and multimodal large models [20].