SMART：同时多智能体递归轨迹预测
SMART: Simultaneous Multi-Agent Recurrent Trajectory Prediction
摘要：
我们提出了解决未来轨迹预测中两个关键挑战的进展：（i）训练数据和预测中的多模态;（ii）不考虑代理数量的恒定时间推断。现有的轨迹预测从根本上受到训练数据缺乏多样性的限制，这很难在足够覆盖可能模式的情况下获得。我们的第一个贡献是一个自动的方法来模拟不同的轨迹在顶视图。它使用预先存在的数据集和地图作为初始化，挖掘现有的轨迹来表示现实的驾驶行为，并使用多智能体车辆动力学模拟器来生成涵盖各种模式并与场景布局约束一致的各种新轨迹。我们的第二个贡献是一种新的方法，产生不同的预测，同时占场景语义和多智能体的相互作用，与恒定的时间推理独立的代理的数量。我们提出了一个具有新型状态池操作和损失的convLSTM，可以在单次向前传递中预测多个代理的场景一致性状态，以及用于多样性的CVAE。我们验证了我们提出的多智能体轨迹预测方法的训练和测试建议的模拟数据集和现有的真实数据集的交通场景。在这两种情况下，我们的方法都大大优于SOTA方法，突出了我们的多样化数据集模拟和恒定时间多样化轨迹预测方法的优势。
英文摘要：
We propose advances that address two key challenges in future trajectory prediction: (i) multimodality in both training data and predictions and (ii) constant time inference regardless of number of agents. Existing trajectory predictions are fundamentally limited by lack of diversity in training data, which is difficult to acquire with sufficient coverage of possible modes. Our first contribution is an automatic method to simulate diverse trajectories in the top-view. It uses pre-existing datasets and maps as initialization, mines existing trajectories to represent realistic driving behaviors and uses a multi-agent vehicle dynamics simulator to generate diverse new trajectories that cover various modes and are consistent with scene layout constraints. Our second contribution is a novel method that generates diverse predictions while accounting for scene semantics and multi-agent interactions, with constant-time inference independent of the number of agents. We propose a convLSTM with novel state pooling operations and losses to predict scene-consistent states of multiple agents in a single forward pass, along with a CVAE for diversity. We validate our proposed multi-agent trajectory prediction approach by training and testing on the proposed simulated dataset and existing real datasets of traffic scenes. In both cases, our approach outperforms SOTA methods by a large margin, highlighting the benefits of both our diverse dataset simulation and constant-time diverse trajectory prediction methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
