点击下方 卡片 ，关注" 自动驾驶之心 "公众号 戳我-> 领取 自动驾驶近30个 方向 学习 路线 名额有限，仅限前「5名」 最近一个月，柱哥在星球内更新了很多最新的行业动态： 同时，还有很多的答疑解惑： Waymo最新的基座模型分享，快慢双系统+数据飞轮； 2025地平线技术生态大会上，苏箐关于自驾的一些insights； 自动驾驶世界模型论文与代码汇总； 英伟达2025年技术图鉴，自驾、具身、大模型全面开花； 理想披露了的最新技术信息，从数据闭环到训练闭环。 近期柱哥也会邀请嘉宾在星球内部和大家聊一聊最近的一些技术进展，欢迎大家加入自动驾驶之心知识星球。 我们准备了大额新人优惠...... 秋招/社招offer建议； 传统规控实现给端到端大模型兜底的思路； 动态行人的场景高斯重建的方法； 40个问题深度解析自动驾驶领域vla+wm的重磅工作：DriveVLA-W0； BEV融合如何能够提升盲区（很近范围）内3D Box的边界准确程度； 小鹏第二代VLA的延展讨论； 对于很多想入门的同学来说，试错成本有点高。没时间和缺乏完整的体系是最大问题，这也容易导致行业壁垒 越来越高，如果想要卷赢那就更加困难了。 扛 ...

Core Viewpoint - The integration of Vision-Language Action (VLA) and World Model (WM) technologies is becoming increasingly evident, suggesting a trend towards unification rather than opposition in the field of autonomous driving [3][5][7]. Group 1: Technology Trends - VLA and WM are seen as complementary technologies, with VLA focusing on abstract reasoning and WM on physical perception, both essential for achieving advanced General Artificial Intelligence (AGI) [4]. - Recent academic explorations have demonstrated the feasibility of combining VLA and WM, with notable projects like DriveVLA-W0 showcasing successful joint training [4]. - The future training pipeline for Level 4 (L4) autonomous systems is expected to incorporate VLA, Reinforcement Learning (RL), and WM, indicating the necessity of all three components [5]. Group 2: Community and Learning Resources - The "Autonomous Driving Heart Knowledge Planet" community has been established to provide a comprehensive platform for learning and sharing knowledge in the autonomous driving sector, with over 4,000 members and plans to expand to nearly 10,000 [10][28]. - The community offers a variety of resources, including video content, learning routes, and Q&A sessions, aimed at both beginners and advanced practitioners in the field [10][12]. - A detailed compilation of over 40 technical routes and numerous datasets related to autonomous driving is available, facilitating quicker access to essential information for newcomers and experienced professionals alike [29][48]. Group 3: Job Opportunities and Networking - The community has established a job referral mechanism with various autonomous driving companies, allowing members to connect with potential employers easily [22]. - Regular discussions and insights from industry leaders are part of the community's offerings, providing members with valuable perspectives on career development and industry trends [14][107].

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] Summary by Sections Introduction to VLA - VLA is categorized into modular VLA, integrated VLA, and reasoning-enhanced VLA, which are essential for improving the reliability and safety of autonomous driving [1][4] Course Overview - A comprehensive course on autonomous driving VLA has been designed, covering foundational algorithms and practical applications, aimed at deepening understanding of the perception systems in autonomous driving [6][21] Course Structure - The course consists of six chapters, starting with an introduction to VLA algorithms, followed by foundational knowledge in Vision, Language, and Action, and culminating in practical assignments [11][19] Chapter Highlights - Chapter 1 provides an overview of VLA algorithms and their development history, along with benchmarks and evaluation metrics [12] - Chapter 2 focuses on the foundational algorithms related to Vision, Language, and Action, including deployment of large models [13] - Chapter 3 discusses VLM (Vision-Language Model) as an interpreter in autonomous driving, covering classic and recent algorithms [14] - Chapter 4 delves into modular and integrated VLA, emphasizing the evolution of language models in planning and control [15] - Chapter 5 explores reasoning-enhanced VLA, introducing new modules for decision-making and action generation [16][18] Practical Applications - The course includes hands-on coding exercises, allowing participants to engage with real-world applications of VLA technologies, such as ReCogDrive and Impromptu VLA [15][18] Learning Outcomes - Participants are expected to gain a thorough understanding of current advancements in VLA, master core algorithms, and apply their knowledge to projects in the autonomous driving field [23][21]

Core Viewpoint - The article discusses the ongoing debate between two approaches in the autonomous driving sector: the VLA (Vision-Language Action) route favored by companies like Xiaopeng, Li Auto, and Yuanrong Qixing, and the World Model (WA) approach promoted by Huawei and NIO. It argues that the WA approach is fundamentally flawed as it relies heavily on data, which is a critical asset in the industry [2][3]. Summary by Sections VLA vs. WA - The VLA approach leverages vast amounts of real-world data to enhance reasoning capabilities, while the WA model seeks to reduce reliance on real data by using simulated data to expand its capabilities. However, the article posits that both approaches are fundamentally about how data is utilized rather than whether data is necessary [2][3]. Data Dependency - Both VLA and WA are built on the premise that "data determines the ceiling" of capabilities. VLA relies on multi-modal data from real scenarios, while WA requires a combination of real and simulated data to enhance its generalization ability. The industry often confuses the "form of data" with its "essence," leading to misconceptions about the role of data in autonomous driving [3]. Industry Insights - The article emphasizes that the real challenge is not whether to depend on data, but how to efficiently utilize it. It highlights that before true artificial intelligence is realized, data will remain the core competitive advantage in the autonomous driving industry [3]. Community and Learning Resources - The article promotes a community platform for knowledge sharing among industry professionals and academics, offering resources such as learning routes, technical discussions, and job opportunities in the autonomous driving field [8][9][18]. Technical Learning and Development - The community provides a comprehensive set of learning materials covering over 40 technical directions in autonomous driving, including VLA, multi-modal models, and various simulation tools, aimed at both beginners and advanced practitioners [19][39]. Networking Opportunities - The platform facilitates networking opportunities with industry leaders and experts, allowing members to engage in discussions about trends, technologies, and career development in the autonomous driving sector [22][92].

Core Insights - The focus of academia and industry has shifted towards VLA (Visual Language Action), which provides human-like reasoning capabilities for more reliable and safer autonomous driving [1][4] - Traditional areas like BEV perception and lane detection have matured, leading to decreased attention from both academia and industry [4] - Major autonomous driving companies are actively developing their own VLA solutions, indicating a competitive landscape [4] Summary by Sections Introduction to Autonomous Driving VLA - VLA is divided into modular VLA, integrated VLA, and reasoning-enhanced VLA, each representing different approaches to autonomous driving [1][4] Course Overview - The course on Autonomous Driving VLA includes detailed explanations of cutting-edge algorithms across the three subfields, supplemented by practical assignments [8] 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 [7] Course Structure - The course is structured into six chapters, covering VLA algorithms, foundational algorithms, VLM as an interpreter, modular and integrated VLA, reasoning-enhanced VLA, and a final project [13][21] Chapter Highlights - Chapter 1 provides an overview of VLA algorithms and their development history, along with benchmarks and evaluation metrics [14] - Chapter 2 focuses on foundational knowledge in Vision, Language, and Action, including the deployment of large models [15] - Chapter 3 discusses VLM's role as an interpreter in autonomous driving, covering classic and recent algorithms [16] - Chapter 4 delves into modular and integrated VLA, emphasizing the evolution of language models in planning and control [17] - Chapter 5 explores reasoning-enhanced VLA, introducing new modules for decision-making and action output [18][20] Learning Outcomes - The course aims to deepen understanding of current advancements in autonomous driving VLA and equip participants with the skills to apply VLA in projects [23][25] Course Logistics - The course starts on October 20 and spans approximately two and a half months, featuring offline video lectures and online Q&A sessions [24]

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].