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].

Core Viewpoint - The article emphasizes the importance of trajectory prediction in the context of autonomous driving and highlights the ongoing relevance of traditional two-stage and modular methods despite the rise of end-to-end approaches. It discusses the integration of trajectory prediction models with perception models as a form of end-to-end training, indicating a significant area of research and application in the industry [1][2]. Group 1: Trajectory Prediction Methods - The article introduces the concept of multi-agent trajectory prediction, which aims to forecast future movements based on the historical trajectories of multiple interacting agents. This is crucial for applications in autonomous driving, intelligent monitoring, and robotic navigation [1]. - It discusses the challenges of predicting human behavior due to its uncertainty and multimodality, noting that traditional methods often rely on recurrent neural networks, convolutional networks, or graph neural networks for social interaction modeling [1]. - The article highlights the advancements in diffusion models for trajectory prediction, showcasing models like Leapfrog Diffusion Model (LED) and Mixed Gaussian Flow (MGF) that have significantly improved accuracy and efficiency in various datasets [2]. Group 2: Course Objectives and Structure - The course aims to provide a systematic understanding of trajectory prediction and diffusion models, helping participants to integrate theoretical knowledge with practical coding skills, ultimately leading to the development of new models and research papers [6][8]. - It is designed for individuals at various academic levels who are interested in trajectory prediction and autonomous driving, offering insights into cutting-edge research and algorithm design [8]. - Participants will gain access to classic and cutting-edge papers, coding implementations, and methodologies for writing and submitting research papers [8][9]. Group 3: Course Highlights and Requirements - The course features a "2+1" teaching model with experienced instructors and dedicated support staff to enhance the learning experience [16][17]. - It requires participants to have a foundational understanding of deep learning and proficiency in Python and PyTorch, ensuring they can engage with the course material effectively [10]. - The course structure includes a comprehensive curriculum covering data sets, baseline codes, and essential research papers, facilitating a thorough understanding of trajectory prediction techniques [20][21][23].