基于模仿学习的端到端本质只是在模仿人类，对物理世界的理解并不透彻。 因此VLA提供了这样一种可能，从模仿人类到成为人类。 业内这两年追捧的端到端，标志着智能驾驶从规则驱动向数据驱动的根本转变。但在实际量产中，端到端虽然提供了一个打通上下游视角的能力，但面对复杂的困难场景 仍然受限。如果在自动驾驶公司工作过，就知道量产模型的迭代仍然被限制在无限corner case的循环中。这里也借用李想AI Talk的一段话： " 端到端比较像什么呢？端到端比较像哺动物的智能，比如像马戏团里的一些动物，向人类学习怎么骑自行车。它学了人类的这些行为，人类怎么去做出各种的行为的开 车。但是它对物理世界并不理解，它只是看到了一个什么样的三维的图像，知道自身的速度，并给出了一个什么样的轨迹，所以它应付大部分的泛化是没有问题的，去面 对它从来没有学到的、特别复杂的，其实就会遇到问题。所以这时候我们也会配合，视觉语言模型 VLM，然后放进来。但是我们能够用到的视觉语言模型这些开源的， 用在交通上的能力都非常的有限，所以只能起到一些非常有限的辅助的一个作用。我觉得第二个阶段就是哺乳动物智能运作的一个方式。 " VLA本质上也可以算作是一种 ...

Core Insights - The article discusses the transition in intelligent driving technology from rule-driven to data-driven approaches, highlighting the emergence of VLA (Vision-Language Action) as a more straightforward and effective method compared to traditional end-to-end systems [1][2] - The challenges in the current VLA technology stack are emphasized, including the complexity and fragmentation of knowledge, which makes it difficult for newcomers to enter the field [2][3] - A new practical course on VLA has been developed to address these challenges, providing a structured learning path for students interested in advanced knowledge in autonomous driving [3][4][5] Summary by Sections Introduction to VLA - The article introduces VLA as a significant advancement in autonomous driving, offering a cleaner approach than traditional end-to-end systems, while also addressing corner cases more effectively [1] Challenges in Learning VLA - The article outlines the difficulties faced by learners in navigating the complex and fragmented knowledge landscape of VLA, which includes a plethora of algorithms and a lack of high-quality documentation [2] Course Development - A new course titled "Autonomous Driving VLA Practical Course" has been created to provide a comprehensive overview of the VLA technology stack, aiming to facilitate easier entry into the field for students [3][4] Course Features - The course is designed to address key pain points, offering quick entry into the subject matter through accessible language and examples [3] - It aims to build a framework for understanding VLA research and enhance research capabilities by teaching students how to categorize papers and extract innovative points [4] - The course includes practical components to ensure that theoretical knowledge is effectively applied in real-world scenarios [5] Course Outline - The course covers various topics, including the origins of VLA, foundational algorithms, and the differences between modular and integrated VLA systems [6][15][19][20] - It also includes practical coding exercises and projects to reinforce learning and application of concepts [22][24][26] Instructor Background - The course is led by experienced instructors with a strong background in multi-modal perception, autonomous driving, and large model frameworks, ensuring high-quality education [27] Learning Outcomes - Upon completion, students are expected to have a thorough understanding of current advancements in VLA, core algorithms, and the ability to apply their knowledge in practical settings [28][29]