Core Viewpoint - The article emphasizes the ongoing relevance and importance of trajectory prediction in autonomous driving, despite the rise of VLA (Vehicle Localization and Awareness) technologies. It highlights that trajectory prediction remains a critical module for ensuring safety and efficiency in driving systems [1][2]. Group 1: Trajectory Prediction Importance - Trajectory prediction is essential for autonomous driving systems as it helps in identifying potential hazards and planning optimal driving routes, thereby enhancing safety and efficiency [1]. - The quality of trajectory prediction directly impacts the planning and control of autonomous vehicles, making it a fundamental component of intelligent driving systems [1]. Group 2: Research and Development in Trajectory Prediction - Academic research in trajectory prediction is thriving, with significant focus on joint prediction, multi-agent prediction, and diffusion-based approaches, which are gaining traction in major conferences [1]. - The introduction of diffusion models has shown promise in improving multi-modal modeling capabilities for trajectory prediction, addressing the challenges posed by human behavior's uncertainty and multi-modality [2][3]. Group 3: Course Offering and Objectives - A new course on trajectory prediction using diffusion models is being offered, aimed at teaching research methods and paper publication strategies, particularly for multi-agent trajectory prediction [2][9]. - The course will cover various aspects, including classic and cutting-edge papers, baseline models, datasets, and writing methodologies, to help students develop a comprehensive understanding of the field [7][9]. Group 4: Course Structure and Content - The course spans 12 weeks of online group research followed by 2 weeks of paper guidance, with a focus on empirical validation using public datasets like ETH, UCY, and SDD [12][24]. - Key topics include the introduction of diffusion models, traditional trajectory prediction methods, and advanced techniques for integrating social interaction modeling and conditional control mechanisms [28][29].