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 ： ...

最近邀请到苹果姐和星友做了一次线上交流，分享给大家。 苹果姐 2020年从国有银行大跨度转行至自动驾驶大厂，后又入职某头部L4创业公司和头部新势力， 研究方向也多次转变：从算法评测开始，又先后从事2D交通红绿灯检测，泊车视觉感知，BEV感 知，端到端主动安全算法等。 无论是转行到自动驾驶，还是之后多次的方向转换，柱哥都学习到很多。我提炼一下有两个关键的 点： 一是机会在面前一定要抓住，付出全力： 2020年转行的时候，投的自驾公司很长时间都没有回信， 最后有一家自驾公司联系苹果姐要求一周后线上机试，苹果姐在没有准备的前提下一周内高强度刷 leetcode最终成功过了机试。也得益于20年自动驾驶扩招，苹果姐也成功转行。 二是先转行再一步步提升，找准赛道： 起初苹果姐从评测开始，虽然不是算法岗但积累了一定的 coding能力。同时借着评测的契机学习了静态感知，之后跳槽也顺利到了感知岗位，然后一步步到 BEV感知再到如今的端到端主动安全，这背后是持续的学习进步和对行业趋势的把握。 最近也有很多同学咨询柱哥方向选择的问题，所以我也是邀请到苹果姐和大家分享这个主题， 直播 回访已经上传到自动驾驶之心知识星球，欢迎大家一 ...

Core Insights - The article emphasizes the establishment of a comprehensive community called "Autonomous Driving Heart Knowledge Planet," aimed at bridging the gap between academia and industry in the field of autonomous driving [1][3][14]. Group 1: Community Development - The community has grown to over 4,000 members and aims to reach nearly 10,000 within two years, providing a platform for technical sharing and communication among beginners and advanced learners [3][14]. - The community offers various resources, including videos, articles, learning paths, Q&A sessions, and job exchange opportunities, making it a holistic hub for autonomous driving enthusiasts [1][3][5]. Group 2: Learning Resources - The community has compiled over 40 technical learning paths, covering topics such as end-to-end learning, multi-modal large models, and data annotation practices, significantly reducing the time needed for research [5][14]. - Members can access a variety of video tutorials and courses tailored for beginners, covering essential topics in autonomous driving technology [9][15]. Group 3: Industry Engagement - The community collaborates with numerous industry leaders and academic experts to discuss trends, technological advancements, and production challenges in autonomous driving [6][10][14]. - There is a mechanism for job referrals within the community, facilitating connections between members and leading companies in the autonomous driving sector [10][12]. Group 4: Technical Focus Areas - The community has organized resources on various technical areas, including 3D object detection, multi-sensor fusion, and high-precision mapping, which are crucial for the development of autonomous driving technologies [27][29][31]. - Specific focus is given to emerging technologies such as visual language models (VLM) and world models, with detailed summaries and resources available for members [37][39][45].

Core Viewpoint - The article emphasizes the establishment of a comprehensive community called "Autonomous Driving Heart Knowledge Planet," aimed at providing a platform for knowledge sharing and networking in the autonomous driving industry, addressing the challenges faced by newcomers in the field [1][3][14]. Group 1: Community Development - The community has grown to over 4,000 members and aims to reach nearly 10,000 within two years, providing a space for technical sharing and communication among beginners and advanced learners [3][14]. - The community integrates various resources including videos, articles, learning paths, Q&A, and job exchange, making it a comprehensive hub for autonomous driving enthusiasts [3][5]. Group 2: Learning Resources - The community has organized over 40 technical learning paths, covering topics such as end-to-end autonomous driving, multi-modal large models, and data annotation practices, significantly reducing the time needed for research [5][14]. - Members can access a variety of video tutorials and courses tailored for beginners, covering essential topics in autonomous driving technology [9][15]. Group 3: Industry Insights - The community regularly invites industry experts to discuss trends, technological advancements, and production challenges in autonomous driving, fostering a serious content-driven environment [6][14]. - Members are encouraged to engage with industry leaders for insights on job opportunities and career development within the autonomous driving sector [10][18]. Group 4: Networking Opportunities - The community facilitates connections between members and various autonomous driving companies, offering resume forwarding services to help members secure job placements [10][12]. - Members can freely ask questions regarding career choices and research directions, receiving guidance from experienced professionals in the field [87][89].

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] 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 learning roadmap for VLA has been designed, covering principles to practical applications, with a focus on core areas such as visual perception, large language models, action modeling, and dataset creation [6] Course Content - The course includes detailed explanations of cutting-edge algorithms like CoT, MoE, RAG, and reinforcement learning, aimed at deepening understanding of autonomous driving perception systems [6] Course Structure - The course is structured into 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 Highlights - Chapter 1 provides an overview of VLA algorithms and their development history, along with benchmarks and evaluation metrics [13] - Chapter 2 delves into foundational algorithms related to Vision, Language, and Action, and discusses the deployment of large models [14] - Chapter 3 focuses on VLM's role as an interpreter in autonomous driving, covering classic and recent algorithms [15] - Chapter 4 discusses modular and integrated VLA, emphasizing the evolution of language models in planning and control [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 will build and fine-tune their own VLA models [20] Learning Outcomes - The course aims to provide a deep understanding of current advancements in VLA, covering three main subfields: VLM as an interpreter, modular & integrated VLA, and reasoning-enhanced VLA [24] - Participants will gain insights into key AI technologies such as visual perception, multimodal large models, and reinforcement learning, enabling them to apply their knowledge in practical projects [24]

Core Viewpoint - The article discusses the evolving landscape of the autonomous driving industry in China, highlighting the shift in competitive dynamics and the increasing investment in autonomous driving technologies as a core focus of AI development [1][2]. Industry Trends - The autonomous driving sector has undergone significant changes over the past two years, with new players entering the market and existing companies focusing on improving execution capabilities [1]. - The industry experienced a flourishing period before 2022, where companies with standout technologies could thrive, but has since transitioned into a more competitive environment that emphasizes addressing weaknesses [1]. - Companies that remain active in the market are progressively enhancing their hardware, software, AI capabilities, and engineering implementation to survive and excel [1]. Future Outlook - By 2025, the industry is expected to enter a "calm period," where unresolved technical challenges in areas like L3, L4, and Robotaxi will continue to present opportunities for professionals in the field [2]. - The article emphasizes the importance of comprehensive skill sets for individuals in the autonomous driving sector, suggesting that those with a short-term profit mindset may not endure in the long run [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 field, featuring over 4,000 members and aiming for a growth to nearly 10,000 in the next two years [4][17]. - The community offers a variety of resources, including video content, learning pathways, Q&A sessions, and job exchange opportunities, catering to both beginners and advanced learners [4][6][18]. - Members can access detailed technical routes and practical solutions for various autonomous driving challenges, significantly reducing the time needed for research and learning [6][18]. Technical Focus Areas - The community has compiled over 40 technical routes related to autonomous driving, covering areas such as end-to-end learning, multi-modal models, and various simulation platforms [18][39]. - There is a strong emphasis on practical applications, with resources available for data processing, 4D labeling, and engineering practices in autonomous driving [12][18]. Job Opportunities - The community facilitates job opportunities by connecting members with openings in leading autonomous driving companies, providing a platform for resume submissions and internal referrals [13][22].

Core Insights - The focus of academia and industry has shifted towards VLA (Vision-Language Action) in autonomous driving, which provides human-like reasoning capabilities for vehicle decision-making [1][4] - Traditional methods in perception and lane detection have matured, leading to decreased attention in these areas, while VLA is now a critical area for development among major autonomous driving companies [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 principles to practical applications, including cutting-edge algorithms like CoT, MoE, RAG, and reinforcement learning [6][12] Course Structure - The course consists of six chapters, starting with an introduction to VLA algorithms, followed by foundational algorithms, VLM as an interpreter, modular and integrated VLA, reasoning-enhanced VLA, and a final project [12][20] Chapter Highlights - Chapter 1 provides an overview of VLA algorithms and their development history, along with benchmarks and evaluation metrics [13] - Chapter 2 focuses on the foundational knowledge of Vision, Language, and Action modules, including the deployment of large models [14] - Chapter 3 discusses VLM's role as an interpreter in autonomous driving, covering classic and recent algorithms [15] - Chapter 4 delves into modular and integrated VLA, emphasizing the evolution of language models in planning and control [16] - Chapter 5 explores reasoning-enhanced VLA, introducing new modules for decision-making and action generation [17][19] Learning Outcomes - The course aims to deepen understanding of VLA's current advancements, core algorithms, and applications in projects, benefiting participants in internships and job placements [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] - 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]