作为研究，VLA至少提供了一种摆脱无尽corner case的可能性！

Core Viewpoint - VLA (Vision-Language-Action) is emerging as a mainstream keyword in autonomous driving, with new players rapidly entering the field and industrial production accelerating, while academia continues to innovate and compete [1][2]. Summary by Sections 1. VLA Research and Development - The VLA model represents a shift from traditional modular architectures to a unified end-to-end model that directly maps raw sensor inputs to driving control commands, addressing previous bottlenecks in autonomous driving technology [3][4]. - Traditional modular architectures (L2-L4) have clear advantages in terms of logic and independent debugging but suffer from cumulative error effects and information loss, making them less effective in complex traffic scenarios [4][5]. 2. VLA Model Advantages - The introduction of VLA models leverages the strengths of large language models (LLMs) to enhance interpretability, reliability, and the ability to generalize to unseen scenarios, thus overcoming limitations of earlier models [5][6]. - VLA models can explain their decision-making processes in natural language, improving transparency and trust in autonomous systems [5][6]. 3. Course Objectives and Structure - The course aims to provide a systematic understanding of VLA, helping participants develop practical skills in model design and research paper writing, while also addressing common challenges faced by newcomers in the field [6][7]. - The curriculum includes 12 weeks of online group research, followed by 2 weeks of paper guidance and 10 weeks of paper maintenance, focusing on both theoretical knowledge and practical coding skills [7][8]. 4. Enrollment and Requirements - The program is designed for a small group of 6 to 8 participants, targeting individuals with a foundational understanding of deep learning and basic programming skills [11][16]. - Participants are expected to engage actively in discussions and complete assignments on time, maintaining academic integrity throughout the course [20][29]. 5. Course Highlights - The course offers a comprehensive learning experience with a multi-faceted teaching approach, including guidance from experienced mentors and a structured evaluation system to track progress [23][24]. - Participants will gain access to essential resources, including datasets and baseline codes, to facilitate their research and experimentation [24][25].