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。 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。 《自动驾驶VLA与大模型实战课程》这门课程包含自动驾驶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！名额仅剩10个 讲师介绍 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。 为此，我们联合清华的教研团队， 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。 咖喱，清华大学硕士生 ： ...

Core Viewpoint - The focus of academia and industry is shifting towards VLA (Visual Language Action), which provides human-like reasoning capabilities for more reliable and safer autonomous driving [1][4]. Summary by Sections Overview of Autonomous Driving VLA - Autonomous driving VLA can be categorized into modular VLA, integrated VLA, and reasoning-enhanced VLA [1]. - Traditional perception methods like BEV (Bird's Eye View) and lane detection are becoming mature, leading to decreased attention from both academia and industry [4]. Key Content of Autonomous Driving VLA - Core components of autonomous driving VLA include visual perception, large language models, action modeling, large model deployment, and dataset creation [7]. - Cutting-edge algorithms such as Chain-of-Thought (CoT), Mixture of Experts (MoE), Retrieval-Augmented Generation (RAG), and reinforcement learning are at the forefront of this field [7]. Course Structure - The course titled "Autonomous Driving VLA and Large Model Practical Course" includes detailed explanations of cutting-edge algorithms in the three subfields of autonomous driving VLA, along with practical assignments [8]. Chapter Summaries 1. **Introduction to VLA Algorithms** - This chapter provides a comprehensive overview of VLA algorithms, their concepts, and development history, along with open-source benchmarks and evaluation metrics [14]. 2. **Algorithm Fundamentals of VLA** - Focuses on foundational knowledge of Vision, Language, and Action modules, and includes a section on deploying and using popular large models [15]. 3. **VLM as an Autonomous Driving Interpreter** - Discusses the role of VLM (Visual Language Model) in scene understanding and covers classic and recent algorithms like DriveGPT4 and TS-VLM [16]. 4. **Modular & Integrated VLA** - Explores the evolution of language models from passive descriptions to active planning components, emphasizing the direct mapping from perception to control [17]. 5. **Reasoning-Enhanced VLA** - Focuses on the trend of integrating reasoning modules into autonomous driving models, highlighting the parallel output of control signals and natural language explanations [18]. 6. **Capstone Project** - Involves practical tasks starting from network construction, allowing participants to customize datasets and fine-tune models, emphasizing hands-on experience [21]. Learning Outcomes - The course aims to advance the understanding of autonomous driving VLA in both academic and industrial contexts, equipping participants with the ability to apply VLA concepts in real-world projects [23]. Course Schedule - The course is set to begin on October 20, with a duration of approximately two and a half months, featuring offline video lectures and online Q&A sessions [24]. Prerequisites - Participants are expected to have a foundational knowledge of autonomous driving, familiarity with transformer models, reinforcement learning, and basic mathematical concepts [25].

Core Insights - The focus of academia and industry is shifting towards VLA (Vision-Language-Action) for enhancing autonomous driving capabilities, providing human-like reasoning in vehicle decision-making processes [1][4] - Traditional methods in perception and lane detection are becoming mature, leading to a decline in interest, while VLA is seen as a critical area for development by major players in the autonomous driving sector [4][6] - A comprehensive learning roadmap for VLA has been designed, covering foundational principles to practical applications [6] Summary by Sections Course Overview - The course titled "Autonomous Driving VLA and Large Model Practical Course" aims to deepen understanding of VLA through detailed explanations of cutting-edge algorithms and practical assignments [6][22] Chapter 1: Introduction to VLA Algorithms - This chapter provides a conceptual overview of VLA algorithms, their historical development, and introduces open-source benchmarks and evaluation metrics relevant to VLA [13] Chapter 2: Algorithm Fundamentals of VLA - Focuses on foundational knowledge in Vision, Language, and Action modules, and includes a section on deploying and using popular open-source large models [14] Chapter 3: VLM as an Autonomous Driving Interpreter - Discusses the role of VLM (Vision-Language Model) in scene understanding prior to the introduction of VLA, covering classic and recent algorithms such as DriveGPT4 and TS-VLM [15] Chapter 4: Modular and Integrated VLA - Explores the evolution of language models from passive descriptions to active planning components, detailing modular and integrated VLA approaches, and includes practical coding exercises [16] Chapter 5: Reasoning-Enhanced VLA - Concentrates on the reasoning-enhanced VLA subfield, introducing new reasoning modules and discussing various algorithms and their applications in autonomous driving [17][19] Chapter 6: Major Project - The final chapter emphasizes hands-on practice, guiding participants through network construction, dataset customization, and model training using the ms-swift framework [20] Learning Requirements and Outcomes - Participants are expected to have a foundational understanding of autonomous driving, large models, and relevant mathematical concepts, with the course designed to equip them with the ability to understand and apply VLA algorithms in practical scenarios [24]

Core Insights - The focus of academia and industry is shifting towards VLA (Vision-Language-Action) for enhancing autonomous driving capabilities, providing human-like reasoning in vehicle decision-making processes [1][4] - The development of autonomous driving VLA is crucial for companies, with a strong emphasis on self-research and innovation in this area [4] Summary by Sections Introduction to Autonomous Driving VLA - VLA is categorized into modular VLA, integrated VLA, and reasoning-enhanced VLA, each contributing to more reliable and safer autonomous driving [1] Course Overview - A comprehensive learning roadmap for autonomous driving VLA has been designed, covering principles to practical applications [4] Core Content of Autonomous Driving VLA - Key topics include visual perception, large language models, action modeling, model deployment, and dataset creation, with advanced 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 cutting-edge algorithms and practical assignments [6] Course Structure - The course consists of six chapters, each focusing on different aspects of VLA, from algorithm introduction to practical applications and project work [11][19] Chapter Highlights - Chapter 1 provides an overview of VLA algorithms and their historical development, along with benchmarks and evaluation metrics [12] - Chapter 2 delves into the foundational algorithms of VLA, including Vision, Language, and Action modules, and discusses the deployment of large models [13] - Chapter 3 focuses on VLM as an interpreter in autonomous driving, analyzing classic and recent algorithms [14] - Chapter 4 explores modular and integrated VLA, emphasizing the evolution of language models in planning and control [15] - Chapter 5 discusses reasoning-enhanced VLA, introducing new modules for decision-making and action output [16] - Chapter 6 involves a major project where participants will build and fine-tune their own models [19] Learning Outcomes - The course aims to advance understanding of VLA in both academic and industrial contexts, equipping participants with the skills to apply VLA concepts in real-world projects [21] Course Schedule - The course is set to begin on October 20, with a structured timeline for each chapter's release [22] Prerequisites - Participants are expected to have a foundational knowledge of autonomous driving, large models, and relevant programming skills [23]

Core Viewpoint - The article discusses the transition in intelligent driving technology from rule-driven to data-driven approaches, highlighting the limitations of end-to-end models in complex scenarios and the potential of VLA (Vision-Language Action) as a more streamlined solution [1][2]. Group 1: Challenges in Learning and Research - The technical stack for autonomous driving VLA has not yet converged, leading to a proliferation of algorithms and making it difficult for newcomers to enter the field [2]. - A lack of high-quality documentation and fragmented knowledge in various domains increases the entry barrier for beginners in autonomous driving VLA research [2]. Group 2: Course Development - A new course titled "Autonomous Driving VLA Practical Course" has been developed to address the challenges faced by learners, focusing on a comprehensive understanding of the VLA technical stack [3][4]. - The course aims to provide a one-stop opportunity to enhance knowledge across multiple fields, including visual perception, language modules, and action modules, while integrating cutting-edge technologies [2][3]. Group 3: Course Features - The course emphasizes quick entry into the subject matter through a Just-in-Time Learning approach, using simple language and case studies to help students grasp core technologies rapidly [3]. - It aims to build a framework for research capabilities, enabling students to categorize papers and extract innovative points to form their own research systems [4]. - Practical application is a key focus, with hands-on sessions designed to complete the theoretical-to-practical loop [5]. Group 4: Course Outline - The course covers the origins of autonomous driving VLA, foundational algorithms, and the differences between modular and integrated VLA [6][10][12]. - It includes practical sessions on dataset creation, model training, and performance enhancement, providing a comprehensive learning experience [12][14][16]. Group 5: Instructor Background - The instructors have extensive experience in multimodal perception, autonomous driving VLA, and large model frameworks, with numerous publications in top-tier conferences [22]. Group 6: Learning Outcomes - Upon completion, students are expected to thoroughly understand the current advancements in autonomous driving VLA and master core algorithms [23][24]. - The course is designed to benefit students in internships, job recruitment, and further academic pursuits in the field [26]. Group 7: Course Schedule - The course is set to begin on October 20, with a structured timeline for unlocking chapters and providing support through online Q&A sessions [27].

Core Viewpoint - The article emphasizes the growing interest and opportunities in the autonomous driving sector, particularly in roles related to end-to-end systems, VLA (Vision-Language Alignment), and reinforcement learning, which are among the highest-paying positions in the AI industry [1][2]. Summary by Sections Community and Learning Resources - The "Autonomous Driving Heart Knowledge Planet" community has over 4,000 members and aims to grow to nearly 10,000 in the next two years, providing a platform for technical sharing and job-related discussions [1]. - The community offers a comprehensive collection of over 40 technical routes, including learning paths for end-to-end autonomous driving, VLA benchmarks, and practical engineering practices [2][5]. - Members can access a variety of resources, including video content, Q&A sessions, and practical problem-solving related to autonomous driving technologies [1][2]. Technical Learning and Career Development - The community provides structured learning paths for beginners, including full-stack courses suitable for those with no prior experience [7][9]. - There are mechanisms for job referrals within the community, connecting members with job openings in various autonomous driving companies [9][11]. - The community regularly engages with industry experts to discuss trends, technological advancements, and challenges in mass production [4][62]. Industry Insights and Trends - The article highlights the need for talent in the autonomous driving industry, particularly for tackling challenges related to L3/L4 level mass production [1]. - There is a focus on the importance of data set iteration speed in relation to technological advancements in the field, especially as AI enters the era of large models [63]. - The community aims to foster a complete ecosystem for autonomous driving, bringing together academic and industrial insights [12][64].

Core Insights - The article focuses on the development and algorithms of Vision-Language Action (VLA) models in autonomous driving over the past two years, providing a comprehensive overview of various research papers and projects in this field [1]. Group 1: VLA Preceding Work - The article mentions several key papers that serve as interpreters for VLA, including "DriveGPT4" and "TS-VLM," which focus on enhancing autonomous driving perception through large language models [3]. - Additional papers like "DynRsl-VLM" are highlighted for their contributions to improving perception in autonomous driving [3]. Group 2: Modular VLA - The article lists various end-to-end VLA models, such as "RAG-Driver" and "OpenDriveVLA," which aim to generalize driving explanations and enhance autonomous driving capabilities [4]. - Other notable models include "DriveMoE" and "LangCoop," which focus on collaborative driving and knowledge-enhanced safe driving [4]. Group 3: Enhanced Reasoning in VLA - The article discusses models like "ADriver-I" and "EMMA," which contribute to the development of general world models and multimodal approaches for autonomous driving [6]. - Papers such as "DiffVLA" and "S4-Driver" are mentioned for their innovative approaches to planning and representation in autonomous driving [6]. Group 4: Community and Resources - The article emphasizes the establishment of a community for knowledge sharing in autonomous driving, featuring over 40 technical routes and inviting industry experts for discussions [7]. - It also highlights the availability of job opportunities and a comprehensive entry-level technical stack for newcomers in the field [12][14]. Group 5: Educational Resources - The article provides a structured learning roadmap for various aspects of autonomous driving, including perception, simulation, and planning control [15]. - It mentions the compilation of numerous datasets and open-source projects to facilitate learning and research in the autonomous driving sector [15].

Core Viewpoint - The article discusses the advancements in end-to-end (E2E) and VLA (Vision-Language Architecture) algorithms in the autonomous driving industry, highlighting their potential to enhance driving capabilities through unified perception and control modeling, despite their higher technical complexity [1][5]. Summary by Sections End-to-End Algorithms - End-to-end approaches are categorized into single-stage and two-stage methods, with the latter focusing more on joint prediction, where perception serves as input for trajectory planning and prediction [3]. - Single-stage end-to-end models include various methods such as UniAD, DiffusionDrive, and Drive-OccWorld, each emphasizing different aspects and likely to be optimized by combining their strengths in production [3][37]. VLA Algorithms - VLA extends the capabilities of large models to enhance scene understanding in production models, with internal discussions on language models as interpreters and various algorithm summaries for modular and unified end-to-end VLA [5][45]. - The community has compiled over 40 technical routes, facilitating quick access to industry applications, benchmarks, and learning pathways [7]. Community and Resources - The community provides a platform for knowledge exchange among members from renowned universities and leading companies in the autonomous driving sector, offering resources such as open-source projects, datasets, and learning routes [19][35]. - A comprehensive technical stack and roadmap for beginners and advanced researchers are available, covering various aspects of autonomous driving technology [12][15]. Job Opportunities and Networking - The community has established job referral mechanisms with multiple autonomous driving companies, encouraging members to connect and share job opportunities [10][17]. - Regular discussions on industry trends, research directions, and practical applications are held, fostering a collaborative environment for learning and professional growth [20][83].