Core Insights - There is a significant demand for end-to-end and VLA (Vision-Language-Action) technical talent in the automotive industry, particularly among major manufacturers and suppliers [1][3] - The industry is evolving from modular production algorithms to end-to-end solutions and now to VLA, with core algorithms involving BEV perception, VLM, diffusion models, reinforcement learning, and world models [3] Group 1: Industry Demand and Trends - The demand for end-to-end and VLA technology talent is high, with inquiries from multiple companies, including three major manufacturers and several suppliers [1] - The industry primarily operates under two paradigms: single-stage and two-stage approaches, with UniAD being a representative of the single-stage model [1] - The end-to-end approach has diversified into various subfields, especially those based on VLA, with a surge in related academic publications and industrial applications in recent years [1] Group 2: Educational Initiatives - The company has launched courses focused on end-to-end and VLA autonomous driving, aimed at helping individuals quickly and efficiently enter these fields [3][12] - The "VLA and Large Model Practical Course" covers VLA from VLM as an autonomous driving interpreter to modular and integrated VLA, including detailed theoretical foundations and practical assignments [3][12] - The "End-to-End and VLA Autonomous Driving Course" focuses on key algorithms and theoretical foundations, including BEV perception, large language models, diffusion models, and reinforcement learning [12][14] Group 3: Instructor Expertise - The courses are led by experts from both academia and industry, with backgrounds in multimodal perception, autonomous driving VLA, and large model frameworks [8][11][14] - Instructors have published numerous papers in top-tier conferences and possess extensive experience in research and practical applications in autonomous driving and large models [8][11][14] Group 4: Target Audience - The courses are designed for individuals with a foundational knowledge of autonomous driving, familiar with basic modules, and concepts such as transformer models, reinforcement learning, and BEV perception [15][16] - Participants are expected to have a background in probability theory, linear algebra, and programming skills in Python and PyTorch [15][16]