自动驾驶VLA全栈学习路线图

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]