Core Insights - The article discusses the transition in intelligent driving technology from rule-driven to data-driven approaches, highlighting the emergence of VLA (Vision-Language Action) as a more straightforward and effective method compared to traditional end-to-end systems [1][2] - The challenges in the current VLA technology stack are emphasized, including the complexity and fragmentation of knowledge, which makes it difficult for newcomers to enter the field [2][3] - A new practical course on VLA has been developed to address these challenges, providing a structured learning path for students interested in advanced knowledge in autonomous driving [3][4][5] Summary by Sections Introduction to VLA - The article introduces VLA as a significant advancement in autonomous driving, offering a cleaner approach than traditional end-to-end systems, while also addressing corner cases more effectively [1] Challenges in Learning VLA - The article outlines the difficulties faced by learners in navigating the complex and fragmented knowledge landscape of VLA, which includes a plethora of algorithms and a lack of high-quality documentation [2] Course Development - A new course titled "Autonomous Driving VLA Practical Course" has been created to provide a comprehensive overview of the VLA technology stack, aiming to facilitate easier entry into the field for students [3][4] Course Features - The course is designed to address key pain points, offering quick entry into the subject matter through accessible language and examples [3] - It aims to build a framework for understanding VLA research and enhance research capabilities by teaching students how to categorize papers and extract innovative points [4] - The course includes practical components to ensure that theoretical knowledge is effectively applied in real-world scenarios [5] Course Outline - The course covers various topics, including the origins of VLA, foundational algorithms, and the differences between modular and integrated VLA systems [6][15][19][20] - It also includes practical coding exercises and projects to reinforce learning and application of concepts [22][24][26] Instructor Background - The course is led by experienced instructors with a strong background in multi-modal perception, autonomous driving, and large model frameworks, ensuring high-quality education [27] Learning Outcomes - Upon completion, students are expected to have a thorough understanding of current advancements in VLA, core algorithms, and the ability to apply their knowledge in practical settings [28][29]

Core Insights - The article emphasizes the growing importance of Vision-Language Alignment (VLA) in the field of autonomous driving, highlighting its dominance in recent conferences and research outputs [1][3]. - VLA enables autonomous vehicles to make decisions in diverse scenarios, moving beyond traditional single-task methods, and offers potential solutions for corner cases [3][4]. Summary by Sections VLA in Autonomous Driving - VLA and its derivatives have become a primary focus for both autonomous driving companies and academic institutions, accounting for nearly half of the advancements in the field [1]. - The technology stack for autonomous driving VLA is still evolving, with numerous algorithms emerging, leading to challenges in entry and understanding [4]. Educational Initiatives - A new course titled "Practical Tutorial on Autonomous Driving VLA" has been developed in collaboration with Tsinghua University to address the challenges faced by learners in this field [5][6]. - The course aims to provide a comprehensive understanding of the VLA technology stack, covering various modules such as visual perception, language, and action [4][5]. Course Features - The course is designed to facilitate quick entry into the field by using a Just-in-Time Learning approach, making complex concepts more accessible [5]. - It aims to build a framework for research capabilities, helping students categorize papers and extract innovative points [6]. - Practical applications are emphasized, with hands-on sessions to bridge theory and practice [7]. Course Outline - The curriculum includes an introduction to VLA algorithms, foundational algorithms, and the role of Vision-Language Models (VLM) as interpreters in autonomous driving [12][14][16]. - It covers modular and integrated VLA approaches, detailing the evolution of language models from passive descriptions to active planning components [18]. - The course also addresses reasoning-enhanced VLA, focusing on long-chain reasoning and memory integration in decision-making processes [20]. Learning Outcomes - Participants are expected to gain a thorough understanding of current advancements in autonomous driving VLA and master core algorithms [25][26]. - The course requires prior knowledge in autonomous driving basics, familiarity with transformer models, and a foundation in probability and linear algebra [28]. Course Schedule - The course is set to commence on October 20, with a duration of approximately two and a half months, featuring offline video lectures and online Q&A sessions [29].