⼀、 端到端离不开的轨迹预测 端到端量产以来，很多规划控制和轨迹预测放同学都很焦虑，都想着转行做感知模型，怕自己过两年失业。但 这一年多以来，据自动驾驶之心了解，一段式端到端上车的并不多，很多公司依然沿用二段式端到端或者模块 化的方法，轨迹预测或者说联合预测仍然是量产使用最多的算法， 依然是许多公司和机构研究的热点。但更 进一步，其实轨迹预测的模型和感知模型融合在一起训练，其实就是所谓的端到端，因此 相关的会议和期刊 依然有较大量的工作产出。 自动驾驶之心针对目前比较火的基于扩散模型的多智能体轨迹预测方法研究展开了首个1v6小班课！本课题聚 焦于"基于扩散模型的多智能体轨迹预测方法"。多智能体轨迹预测旨在根据多个交互主体的历史轨迹，预测其 未来运动轨迹，这在自动驾驶、智能监控和机器人导航等场景中至关重要。然而，由于人的行为具有不确定性 和多模态性，预测任务十分困难。传统方法通常依赖循环神经网络、卷积网络或图神经网络建模社会交互，而 生成模型（如GAN和CVAE）虽然可以模拟多模态分布，但效率不高。 扩散模型是一类通过逐步去噪实现复杂分布生成的新型模型，近年来在图像生成等领域取得了重大突破。研究 者发现将扩散模 ...

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].

⼀、课题简介⭐ 基于扩散模型的多智能体轨迹预测方法研究来啦！本课题聚焦于"基于扩散模型的多智能体轨迹预测方法"。多 智能体轨迹预测旨在根据多个交互主体的历史轨迹，预测其未来运动轨迹，这在自动驾驶、智能监控和机器人 导航等场景中至关重要。然而，由于人的行为具有不确定性和多模态性，预测任务十分困难。传统方法通常依 赖循环神经网络、卷积网络或图神经网络建模社会交互，而生成模型（如GAN和CVAE）虽然可以模拟多模态 分布，但效率不高。 扩散模型是一类通过逐步去噪实现复杂分布生成的新型模型，近年来在图像生成等领域取得了重大突破。研究 者发现将扩散模型应用于轨迹预测可以显著提升多模态建模能力。例如，LeapfrogDiffusionModel（LED）采 用可训练的"跳跃"初始化器，减少去噪步骤并实现实时预测，在NBA/NFL/SDD/ETHUCY等数据集上显著提升 精度并加速了19–30倍。MixedGaussianFlow（MGF）通过构建混合高斯先验来更好地匹配未来轨迹的多峰分 布，在UCY/ETH和SDD数据集上达到了最先进性能。此外，Pattern Memory-based Diffusion Model （ ...