这几个月其实很多小伙伴联系柱哥咨询未来的建议，有工作两三年的也有硕士甚至本科生。他们在刚接触这个领域时，往往会遇到很多问题。从模块化的量产算 法发展到端到端，再到如今的VLA。核心算法涉及BEV感知、视觉语言模型VLM、扩散模型、强化学习、世界模型等等。通过学习端到端与VLA自动驾驶，可以 掌握学术界和工业界最前沿的技术方向。据现有行业的发展来看，端到端和VLA的岗位快要饱和，留下的窗口期没多久了...... 很多同学的咨询如何快速高效的入门端到端和VLA。因此自动驾驶之心联合了 工业界 和 学术界 的大佬开展了 《端到端与VLA自动驾驶小班课》 和 《自动驾驶 VLA和大模型实战课程》 ！ 扫码报名！优惠名额仅剩6个 扫码报名！抢占课程名额 课程大纲 自动驾驶VLA与大模型实战课程 由学术界大佬带队！ 这门课程聚焦在VLA领域，从VLM作为自动驾驶解释器开始，到模块化VLA、一体化VLA，再到当前主流的推理增强VLA。三大自动驾驶 VLA领域全面梳理， 非常适合刚接触大模型、VLA的同学。 课程也配套了详细的理论基础梳理，Vision/Language/Acition三大模块、强化学习、扩散模型等等基 础， ...

Core Insights - The article discusses the evolution of end-to-end algorithms in autonomous driving, highlighting the transition from modular production algorithms to end-to-end and now to Vision-Language Alignment (VLA) models [1][3] - It emphasizes the rich technology stack involved in end-to-end algorithms, including BEV perception, visual language models (VLM), diffusion models, reinforcement learning, and world models [3] Summary by Sections End-to-End Algorithms - End-to-end algorithms are categorized into two main paradigms: single-stage and two-stage, with UniAD being a representative of the single-stage approach [1] - Single-stage can further branch into various subfields, particularly those based on VLA, which have seen a surge in related publications and industrial applications in recent years [1] Courses Offered - The article promotes two courses: "End-to-End and VLA Autonomous Driving Small Class" and "Practical Course on Autonomous Driving VLA and Large Models," aimed at helping individuals quickly and efficiently enter the field [3] - The "Practical Course" focuses on VLA, covering topics from VLM as an autonomous driving interpreter to modular and integrated VLA, along with detailed theoretical foundations [3][12] Instructor Team - The instructor team includes experts from both academia and industry, with backgrounds in multi-modal perception, autonomous driving VLA, and large model frameworks [8][11][14] - Notable instructors have published numerous papers in top-tier conferences and have extensive experience in research and practical applications in autonomous driving and large models [8][11][14] Target Audience - The courses are designed for individuals with a foundational understanding of autonomous driving, familiar with basic modules, and have knowledge of transformer models, reinforcement learning, and BEV perception [15][17]

Core Insights - The article discusses the evolution of end-to-end algorithms in autonomous driving, highlighting the transition from modular production algorithms to end-to-end and now to Vision-Language Alignment (VLA) models [1][3] - It emphasizes the rich technology stack involved in end-to-end algorithms, including BEV perception, visual language models (VLM), diffusion models, reinforcement learning, and world models [3][10] Summary by Sections End-to-End Algorithms - End-to-end algorithms are categorized into two main paradigms: single-stage and two-stage, with UniAD being a representative of the single-stage approach [1] - Single-stage can further branch into various subfields, particularly those based on VLA, which have seen a surge in related publications and industrial applications in recent years [1] VLA and Course Offerings - The article mentions the launch of courses aimed at helping individuals quickly and efficiently learn about end-to-end and VLA in autonomous driving, featuring collaboration between industry and academia [3] - The "VLA and Large Model Practical Course" focuses on VLA, covering topics from VLM as an autonomous driving interpreter to modular and integrated VLA approaches [3] Course Structure and Faculty - The course structure includes a comprehensive overview of VLA, with detailed theoretical foundations in Vision, Language, and Action, as well as practical assignments to build VLA models and datasets from scratch [3][10] - The teaching team consists of experienced professionals from top academic institutions and industry, with backgrounds in multimodal perception, autonomous driving, and large model frameworks [7][9][10] Target Audience and Requirements - The courses are designed for individuals with a foundational understanding of autonomous driving and familiarity with key technologies such as transformer models, reinforcement learning, and BEV perception [13] - Participants are expected to have a basic knowledge of probability theory, linear algebra, and programming skills in Python and PyTorch [13]