Core Viewpoint - The article discusses the rapid advancements in large models and their applications in the autonomous driving sector, particularly focusing on the DriveVLM algorithm developed by Tsinghua University and Li Auto to address long-tail problems in real-world driving scenarios [2]. Group 1: DriveVLM Overview - DriveVLM aims to tackle the challenges faced in the transition from Level 2 (L2) to Level 4 (L4) autonomous driving, particularly the infinite long-tail problems that arise in real-world scenarios [2]. - The industry has recognized that data-driven approaches alone may not suffice to evolve towards true L4 autonomous driving, necessitating further exploration of next-generation solutions [2]. Group 2: Innovations of DriveVLM - DriveVLM introduces several innovations, including: - Chain-of-Thought (CoT) for scene description, analysis, and hierarchical planning [4]. - DriveVLM-Dual, which integrates DriveVLM with traditional modules for real-time planning and enhanced spatial reasoning capabilities [4]. - A comprehensive data mining and annotation process to construct the Corner Case dataset, SUP-AD [4]. Group 3: Course Structure and Content - The article outlines a course on multi-modal large models, covering: - Introduction to multi-modal large models, including foundational concepts and applications [21]. - Basic modules of multi-modal large models, explaining components like modality encoders and projectors [23]. - General multi-modal large models, focusing on algorithms for various tasks [25]. - Fine-tuning and reinforcement learning techniques essential for model development [28]. - Applications of multi-modal large models in autonomous driving, highlighting DriveVLM as a key algorithm [30]. - Job preparation related to multi-modal large models, addressing industry needs and interview preparation [32].