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]