上岸自动驾驶感知！轨迹预测1v6小班课仅剩最后一个名额~

Group 1 - The core viewpoint of the article emphasizes the importance of trajectory prediction in autonomous driving and related fields, highlighting that end-to-end methods are not yet widely adopted, and trajectory prediction remains a key area of research [1][3]. - The article discusses the integration of diffusion models into trajectory prediction, which significantly enhances multi-modal modeling capabilities, with specific models like Leapfrog Diffusion Model (LED) achieving real-time predictions and improving accuracy by 19-30 times on various datasets [2][3]. - The course aims to provide a systematic understanding of trajectory prediction, combining theoretical knowledge with practical coding skills, and assisting students in developing their own models and writing research papers [6][8]. Group 2 - The target audience for the course includes graduate students and professionals in trajectory prediction and autonomous driving, who seek to enhance their research capabilities and understand cutting-edge developments in the field [8][10]. - The course offers a comprehensive curriculum that includes classic and cutting-edge papers, baseline codes, and methodologies for selecting research topics, conducting experiments, and writing papers [20][30]. - The course structure includes 12 weeks of online group research followed by 2 weeks of paper guidance, ensuring participants gain practical experience and produce a research paper draft by the end of the program [31][35].