传统的感知被嫌弃，VLA逐渐成为新秀...

Core Viewpoint - The article discusses the evolution of autonomous driving technology, emphasizing the transition from traditional modular architectures to end-to-end models, and highlights the emergence of Vision-Language-Action (VLA) models as a new paradigm in the field [2][3]. Summary by Sections VLA Research Paper Guidance - The course aims to provide systematic knowledge on VLA, addressing gaps in understanding and practical application, and helping students develop their own research ideas and writing skills [4][5][6]. Course Objectives - The program seeks to help students who lack a clear knowledge framework, have difficulty in practical implementation, and struggle with writing and submitting papers [4][5][6]. Course Structure - The course consists of 12 weeks of online group research, followed by 2 weeks of paper guidance and a 10-week maintenance period, focusing on classic and cutting-edge papers, coding skills, and writing methodologies [5][10][12]. Enrollment Details - The program is limited to 6-8 students per session, targeting individuals with a background in deep learning and basic knowledge of autonomous driving algorithms [9][11][14]. Course Highlights - The curriculum includes foundational courses in Python and deep learning, with a focus on enhancing coding abilities and understanding key algorithms and their advantages [18][21][22]. Key Papers and Resources - The course provides access to essential papers and datasets relevant to VLA and autonomous driving, facilitating a comprehensive understanding of the subject matter [23][24][30].