端到端之后，学术界和工业界聚焦的方向是什么？无疑是VLA。VLA提供了类人思考的能力，把车辆决策的过程通过思维链的形式展现出来，从而提 供 更可靠更安全的自动驾驶能力。 自动驾驶VLA目前可以分为模块化VLA、一体化VLA和推理增强VLA三个子领域。 而传统的BEV感知、车道线、Occupancy等方向相对成熟了，无论是学术界或工业界关注度都在逐渐下降。目前 自动驾驶VLA是各家企业急需攻克的方 案。主流的自动驾驶企业，无论是智驾方案供应商还是车企，都在发力自动驾驶VLA的自研。 我们花了三个月的时间设计了一套自动驾驶VLA的学习路 线图，从原理到实战细致展开。 自动驾驶VLA涉及的核心内容包括视觉感知、大语言模型、Action建模、大模型部署、数据集制作等等。最前沿的算法包括CoT、MoE、RAG、强化学 习。通过学习VLA，可以让自己对自动驾驶的感知系统有更深刻的认知。 为此我们联合 清华大学的教研团队 开展了这门《自动驾驶VLA与大模型实战课程》！课程包含自动驾驶VLA三个子领域前沿算法的细致讲解，并会配备 两个实战及一个课程大作业深入理解自动驾驶VLA。 扫码报名！抢占课程名额 讲师介绍 咖喱，清华大 ...

端到端之后，学术界和工业界聚焦的方向是什么？无疑是VLA。VLA提供了类人思考的能力，把车辆决策的过程通过思维链的形式展现出来，从而提供 更可靠更安全的自动驾驶能力。 自动驾驶VLA目前可以分为模块化VLA、一体化VLA和推理增强VLA三个子领域。 扫码报名！抢占课程名额 讲师介绍 咖喱，清华大学硕士生 ：在ICCV/IROS/EMNLP/Nature Communications发表论文若干篇。目前从事多模态感知、自动驾驶VLA、大模型Agent等前沿算法 的预研，并已主持和完成多项自动驾驶感知和大模型框架工具，拥有丰富的自动驾驶、大模型研发和实战经验。 从技术的成熟度及就业的需求来看，自动驾驶VLA是各家企业急需攻克的方案。主流的自动驾驶企业，无论是智驾方案供应商还是车企，都在发力自动 驾驶VLA的自研。 我们花了三个月的时间设计了一套自动驾驶VLA的学习路线图，从原理到实战细致展开。 自动驾驶VLA涉及的核心内容包括视觉感知、大语言模型、Action建模、大模型部署、数据集制作等等。最前沿的算法包括CoT、MoE、RAG、强化学 习。通过学习VLA，可以让自己对自动驾驶的感知系统有更深刻的认知。 为此我 ...

Core Viewpoint - The focus of academia and industry after end-to-end systems is on VLA (Vision-Language-Action), which provides human-like reasoning capabilities for safer and more reliable autonomous driving [1][4]. Summary by Sections Introduction to Autonomous Driving VLA - VLA is categorized into modular VLA, integrated VLA, and reasoning-enhanced VLA, which are essential for advancing autonomous driving technology [1][4]. Technical Maturity and Employment Demand - The demand for autonomous driving VLA solutions is high among major companies, prompting them to invest in self-research and development [4]. Course Overview - A comprehensive learning roadmap for autonomous driving VLA has been designed, covering principles to practical applications [4][6]. Core Content of Autonomous Driving VLA - Key topics include visual perception, large language models, action modeling, model deployment, and dataset creation, with cutting-edge algorithms like CoT, MoE, RAG, and reinforcement learning [6]. Course Collaboration - The course is developed in collaboration with Tsinghua University's research team, featuring detailed explanations of algorithms and practical assignments [6]. Course Structure - The course consists of six chapters, each focusing on different aspects of VLA, including algorithm introduction, foundational algorithms, VLM as an interpreter, modular and integrated VLA, reasoning-enhanced VLA, and a final project [12][20]. Chapter Details - Chapter 1 covers the concept and history of VLA algorithms, including benchmarks and evaluation metrics [13]. - Chapter 2 focuses on foundational algorithms related to Vision, Language, and Action, along with model deployment [14]. - Chapter 3 discusses VLM's role as an interpreter in autonomous driving, highlighting key algorithms [15]. - Chapter 4 delves into modular and integrated VLA, emphasizing the evolution of language models in planning [16]. - Chapter 5 explores reasoning-enhanced VLA, introducing new modules for decision-making and action output [17]. - Chapter 6 involves a hands-on project where participants build and fine-tune their models [20]. Learning Outcomes - The course aims to deepen understanding of VLA's current advancements and core algorithms, equipping participants with practical skills for future research and applications in the autonomous driving sector [22][26]. Course Schedule - The course is set to begin on October 20, with a structured timeline for each chapter's release [23]. Prerequisites - Participants are expected to have a foundational knowledge of autonomous driving, large models, reinforcement learning, and programming skills in Python and PyTorch [26].