传统的感知被嫌弃，VLA逐渐成为新秀...

Core Viewpoint - The article discusses the advancements in the VLA (Vision-Language Action) driver model by Li Auto, highlighting its four core capabilities: spatial understanding, reasoning, communication and memory, and behavioral capabilities. It emphasizes the significance of VLA in the field of autonomous driving, indicating a shift in focus from traditional perception and planning tasks to large models and VLA technologies [2][4]. Summary by Sections VLA Model Capabilities - The VLA model integrates dynamic targets, static elements, navigation maps, and spatial understanding, showcasing a more human-like reasoning ability. This positions VLA as a leading focus in both academia and industry for autonomous driving [2]. Shift in Research Focus - Traditional perception and planning tasks are becoming less prominent in top conferences, with academia increasingly shifting towards large models and VLA. Despite this, the industry continues to optimize traditional methods, indicating ongoing opportunities in both areas [4]. Educational Program - An educational program is introduced to help students systematically grasp key theoretical knowledge in VLA, enhance practical coding skills, and develop their own research ideas. The program includes a structured 12-week online group research course followed by 2 weeks of paper guidance and a 10-week maintenance period [5][34]. Course Structure - The course spans 14 weeks, covering topics from introductory lessons to advanced VLA models and paper writing methodologies. Each week focuses on different aspects of VLA and autonomous driving, culminating in a final project report and submission guidance [8][10][35]. Target Audience - The program is designed for master's and doctoral students in VLA and autonomous driving, individuals seeking to enhance their resumes for further studies abroad, and professionals in the AI and autonomous driving sectors looking to deepen their algorithmic knowledge [14][24]. Course Requirements - Participants are expected to have a foundational understanding of deep learning, basic programming skills in Python, and familiarity with PyTorch. Access to high-performance computing resources is recommended for optimal learning [20][21]. Course Highlights - The program features a "2+1" teaching model with experienced instructors, ensuring comprehensive support throughout the learning process. It emphasizes academic integrity and provides a structured evaluation system to enhance the learning experience [22][23].