当前的自动驾驶VLA，还有很多模块需要优化...

Core Viewpoint - VLA (Vision-Language-Action) is emerging as a mainstream keyword in autonomous driving, with rapid advancements in both academia and industry, aiming to overcome the limitations of traditional modular architectures and enhance the capabilities of autonomous systems [1][5]. Summary by Sections VLA Research and Development - The transition from traditional modular architectures to end-to-end models is marked by the introduction of VLA, which aims to unify sensor inputs directly into driving commands, addressing previous bottlenecks in the development of autonomous driving systems [2][5]. - The VLA model leverages large language models (LLMs) to enhance reasoning, explanation, and interaction capabilities, making it a significant advancement in the field [5]. Traditional Modular Architecture - Early autonomous driving systems (L2-L4) utilized a modular design, where each module (e.g., object detection, trajectory prediction) was developed independently, leading to issues such as error accumulation and information loss [3]. - The limitations of traditional architectures include reliance on manually designed rules, making it difficult to handle complex traffic scenarios [3][4]. Emergence of Pure Vision End-to-End Models - The rise of pure vision end-to-end models, exemplified by NVIDIA's DAVE-2 and Wayve, aimed to simplify system architecture through imitation learning, but faced challenges related to transparency and generalization in unseen scenarios [4][5]. VLA Paradigm - The VLA paradigm introduces a new approach where language serves as a bridge between perception and action, enhancing the model's interpretability and trustworthiness [5]. - VLA models can utilize pre-trained knowledge from LLMs to better understand complex traffic situations and make logical decisions, improving generalization to novel scenarios [5]. Course Objectives and Structure - The course aims to provide a systematic understanding of VLA, addressing gaps in knowledge and practical skills, and includes a comprehensive curriculum covering various aspects of VLA research [6][12]. - The program consists of 12 weeks of online group research, followed by 2 weeks of paper guidance, and an additional 10 weeks for paper maintenance, focusing on both theoretical and practical applications [7][30]. Enrollment and Requirements - The course is designed for individuals with a background in deep learning and basic knowledge of autonomous driving algorithms, requiring familiarity with Python and PyTorch [16][19]. - The class size is limited to 6-8 participants to ensure personalized attention and effective learning [11]. Course Highlights - Participants will gain insights into classic and cutting-edge papers, coding skills, and methodologies for writing and submitting research papers, enhancing their academic and professional profiles [12][15][30].