学术界和工业界都在如何研究端到端与VLA？三个月搞定端到端自动驾驶！

Core Viewpoint - The article discusses the evolution and current state of end-to-end algorithms in autonomous driving, highlighting the emergence of various subfields, particularly those based on Visual Language Models (VLA) and the increasing interest in these technologies within both academia and industry [1][3]. Summary by Sections End-to-End Algorithms - End-to-end algorithms are central to the current mass production of autonomous driving technologies, involving a rich technology stack. There are primarily two paradigms: single-stage and two-stage. The single-stage approach, exemplified by UniAD, directly models vehicle trajectories from sensor inputs, while the two-stage approach outputs trajectories based on perception results [1]. VLA and Related Technologies - The development has progressed from modular production algorithms to end-to-end systems and now to VLA. Key technologies involved include BEV perception, Visual Language Models (VLM), diffusion models, reinforcement learning, and world models. The article emphasizes the importance of understanding these technologies to grasp the cutting-edge directions in both academia and industry [3]. Courses Offered - The article promotes two courses aimed at helping individuals quickly and efficiently learn about end-to-end and VLA in autonomous driving. The courses are designed for those new to large models and VLA, covering foundational theories and practical applications [3][10]. Course Content - The "VLA and Large Model Practical Course" focuses on VLA, starting from VLM as an interpreter for autonomous driving, and covers modular and integrated VLA, as well as mainstream inference-enhanced VLA. It includes detailed theoretical foundations and practical assignments to build VLA models and datasets from scratch [3][10]. Instructor Team - The courses are led by experienced instructors from both academia and industry, with backgrounds in multi-modal perception, autonomous driving VLA, and large model frameworks. They have published numerous papers in top conferences and have substantial practical experience in the field [7][9][10]. Target Audience - The courses are aimed at individuals with a foundational understanding of autonomous driving, familiar with basic modules, and possessing knowledge of transformer models, reinforcement learning, and BEV perception. A background in probability theory, linear algebra, and programming in Python and PyTorch is also recommended [13].