正式开课！端到端与VLA自动驾驶小班课，优惠今日截止~

Core Viewpoint - The article emphasizes the significance of VLA (Vision-Language Alignment) as a new milestone in the mass production of autonomous driving technology, highlighting the progressive development from E2E (End-to-End) to VLA, and the growing interest from professionals in transitioning to this field [1][11]. Course Overview - The course titled "End-to-End and VLA Autonomous Driving Small Class" aims to provide in-depth knowledge of E2E and VLA algorithms, addressing the challenges faced by individuals looking to transition into this area [1][12]. - The curriculum is designed to cover various aspects of autonomous driving technology, including foundational knowledge, advanced models, and practical applications [5][15]. Course Structure - Chapter 1: Introduction to End-to-End Algorithms, covering the historical development and the transition from modular to end-to-end approaches, including the advantages and challenges of each paradigm [17]. - Chapter 2: Background knowledge on E2E technology stacks, focusing on key areas such as VLA, diffusion models, and reinforcement learning, which are crucial for future job interviews [18]. - Chapter 3: Exploration of two-stage end-to-end methods, discussing notable algorithms and their advantages compared to one-stage methods [18]. - Chapter 4: In-depth analysis of one-stage end-to-end methods, including various subfields like perception-based and world model-based approaches, culminating in the latest VLA techniques [19]. - Chapter 5: Practical assignment focusing on RLHF (Reinforcement Learning from Human Feedback) fine-tuning, providing hands-on experience with pre-training and reinforcement learning modules [21]. Target Audience and Learning Outcomes - The course is aimed at individuals with a foundational understanding of autonomous driving and related technologies, such as transformer models and reinforcement learning [28]. - Upon completion, participants are expected to achieve a level equivalent to one year of experience as an end-to-end autonomous driving algorithm engineer, mastering various methodologies and being able to apply learned concepts to real-world projects [28].