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 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 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].