Core Viewpoint - The article discusses the ongoing relevance of trajectory prediction in the context of end-to-end models, highlighting that many companies still utilize layered approaches where trajectory prediction remains a key algorithmic focus. This includes both joint trajectory prediction and target trajectory prediction, which continue to be active research areas with significant output in conferences and journals [1]. Group 1: Trajectory Prediction Research - The article emphasizes the importance of multi-agent trajectory prediction, which aims to forecast future movements based on historical trajectories of multiple interacting entities, crucial for applications in autonomous driving, intelligent monitoring, and robotic navigation [1]. - Traditional methods for trajectory prediction often rely on recurrent neural networks, convolutional networks, or graph neural networks, while generative models like GANs and CVAEs, although capable of simulating multimodal distributions, are noted for their inefficiency [1]. Group 2: Diffusion Models - Diffusion models have emerged as a new class of models that generate complex distributions through a stepwise denoising process, achieving significant breakthroughs in image generation and showing promise in trajectory prediction by enhancing multimodal modeling capabilities [2]. - Specific models such as the Leapfrog Diffusion Model (LED) and Mixed Gaussian Flow (MGF) have demonstrated substantial improvements in accuracy and efficiency, with LED achieving real-time predictions and MGF enhancing diversity in trajectory predictions [2]. Group 3: Course Objectives and Structure - The course aims to provide a systematic understanding of trajectory prediction and diffusion models, helping participants integrate theoretical knowledge with practical coding skills, and develop their own research ideas [6]. - Participants will gain insights into writing and submitting academic papers, with a focus on accumulating a methodology for writing and receiving guidance on revisions and submissions [6]. Group 4: Target Audience and Outcomes - The course is designed for graduate students and professionals in trajectory prediction and autonomous driving, aiming to enhance their resumes and research capabilities [8]. - Expected outcomes include a comprehensive understanding of classic and cutting-edge papers, coding implementations, and the development of a research paper draft [8][9]. Group 5: Course Highlights and Requirements - The course features a "2+1" teaching model with experienced instructors and a structured learning experience, ensuring comprehensive support throughout the research process [16][17]. - Participants are required to have a foundational understanding of deep learning and proficiency in Python and PyTorch, with recommendations for hardware specifications to facilitate learning [10][12].

Core Viewpoint - The article discusses the ongoing relevance of trajectory prediction in the context of end-to-end models, highlighting that many companies still utilize layered approaches where trajectory prediction remains a key algorithmic focus. The article emphasizes the significance of multi-agent trajectory prediction methods based on diffusion models, which are gaining traction in various applications such as autonomous driving and intelligent monitoring [1][2]. Group 1: Trajectory Prediction Research - Despite the rise of end-to-end models, trajectory prediction continues to be a hot research area, with significant output in conferences and journals [1]. - Multi-agent trajectory prediction aims to forecast future movements based on historical trajectories of multiple interacting agents, which is crucial in fields like autonomous driving and robotics [1]. - Traditional methods often struggle with the uncertainty and multimodality of human behavior, while generative models like GANs and CVAEs, although capable of simulating multimodal distributions, lack efficiency [1]. Group 2: Diffusion Models - Diffusion models have emerged as a new class of models that achieve complex distribution generation through gradual denoising, showing significant breakthroughs in image generation and other fields [2]. - The Leapfrog Diffusion Model (LED) enhances real-time prediction by reducing denoising steps, achieving a 19-30 times speedup while improving accuracy on various datasets [2]. - Mixed Gaussian Flow (MGF) and Pattern Memory-based Diffusion Model (MPMNet) are also highlighted for their advanced performance in trajectory prediction by better matching multimodal distributions and utilizing human motion patterns, respectively [2]. Group 3: Course Objectives and Structure - The course aims to provide a systematic understanding of trajectory prediction and diffusion models, helping students integrate theoretical knowledge with practical coding skills [6]. - It addresses common challenges faced by students, such as lack of direction and difficulties in reproducing research papers, by offering a structured approach to model development and academic writing [6]. - The course includes a comprehensive curriculum that covers classic and cutting-edge papers, coding implementations, and writing methodologies, ultimately guiding students to produce a draft of a research paper [6][9]. Group 4: Target Audience and Requirements - The course is designed for graduate students and professionals in trajectory prediction and autonomous driving, aiming to enhance their research capabilities and resume value [8]. - Participants are expected to have a foundational understanding of deep learning and familiarity with Python and PyTorch [10]. - The course emphasizes the importance of academic integrity and active participation, with specific requirements for attendance and assignment completion [15]. Group 5: Course Highlights and Outcomes - The program features a "2+1" teaching model with experienced instructors providing comprehensive support throughout the learning process [16][17]. - Students will gain access to datasets, baseline codes, and essential papers, facilitating a deeper understanding of the subject matter [20][21]. - Upon completion, students will have produced a research paper draft, a project completion certificate, and potentially a recommendation letter based on their performance [19].