Bridging the past to the future, connecting agents both spatially and temporally, lies at the core of the trajectory prediction task. Despite great efforts, it remains challenging to explicitly learn and predict latencies, the temporal delays with which agents respond to different trajectory-changing events and adjust their future paths, whether on their own or interactively. Different agents may exhibit distinct latency preferences for noticing, processing, and reacting to any specific trajectory-changing event. The lack of consideration of such latencies may undermine the causal continuity of the forecasting system and also lead to implausible or unintended trajectories. Inspired by the reverberation curves in acoustics, we propose a new reverberation transform and the corresponding Reverberation (short for Rev) trajectory prediction model, which simulates and predicts different latency preferences of each agent as well as their stochasticity by using two explicit and learnable reverberation kernels, allowing for the controllable trajectory prediction based on these forecasted latencies. Experiments on multiple datasets, whether pedestrians or vehicles, demonstrate that Rev achieves competitive accuracy while revealing interpretable latency dynamics across agents and scenarios. Qualitative analyses further verify the properties of the proposed reverberation transform, highlighting its potential as a general latency modeling approach.

翻译：连接过去与未来，在空间和时间上关联智能体，是轨迹预测任务的核心。尽管已有大量努力，但显式学习和预测延迟——即智能体响应不同轨迹变化事件并调整其未来路径（无论是自主还是交互式）的时间延迟——仍然具有挑战性。不同的智能体对于注意、处理和响应任何特定轨迹变化事件可能表现出不同的延迟偏好。忽视此类延迟可能削弱预测系统的因果连续性，并导致不合理或非预期的轨迹。受声学中回响曲线的启发，我们提出了一种新的回响变换及相应的Reverberation（简称Rev）轨迹预测模型，该模型通过使用两个显式且可学习的回响核来模拟和预测每个智能体的不同延迟偏好及其随机性，从而允许基于这些预测延迟进行可控的轨迹预测。在多个数据集（无论是行人还是车辆）上的实验表明，Rev在实现竞争性精度的同时，揭示了跨智能体和场景的可解释延迟动态。定性分析进一步验证了所提回响变换的特性，突显了其作为通用延迟建模方法的潜力。