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]. Summary by Sections Introduction to VLA - The article emphasizes the ongoing challenges in the VLA technology stack, noting the proliferation of algorithms and the difficulties faced by newcomers in navigating this complex field [2]. Course Development - A new course titled "Practical Tutorial on Autonomous Driving VLA" has been developed in collaboration with academic teams to address the challenges in learning VLA technology, providing a comprehensive overview of the technical stack involved [2][3]. Course Features - The course is designed to: - Address pain points and facilitate quick entry into the field through accessible language and case studies [3]. - Build a framework for research capabilities by helping students categorize papers and extract innovative points [4]. - Combine theory with practice, ensuring a complete learning loop [5]. Course Outline - The course covers various topics, including the origins of VLA, foundational algorithms, and the construction of datasets for VLA [6][15][19]. Chapter Breakdown - **Chapter 1**: Overview of VLA algorithms and their historical development, including benchmarks and evaluation metrics [15]. - **Chapter 2**: Focus on foundational algorithms related to Vision, Language, and Action modules, including deployment of large models [17]. - **Chapter 3**: Discussion of VLM as an interpreter in autonomous driving, covering classic and cutting-edge algorithms [19]. - **Chapter 4**: Examination of modular and integrated VLA, detailing the evolution of language models in planning and control [21]. - **Chapter 5**: Exploration of reasoning-enhanced VLA, emphasizing the integration of reasoning modules in decision-making processes [24]. - **Chapter 6**: A major project where students will build their own networks and datasets, focusing on practical application [26]. Instructor Background - The course is led by experienced instructors with a strong background in multimodal perception, autonomous driving VLA, and large model frameworks [27]. Learning Outcomes - Upon completion, students are expected to have a thorough understanding of current advancements in VLA, core algorithms, and practical applications in projects [29][31].