Reverberation: Learning the Latencies Before Forecasting Trajectories

2025-11-14

AI Summary

In trajectory prediction, ignoring agent-specific response latency differences to motion events (e.g., sudden braking, turning) undermines causal fidelity and behavioral plausibility. This work is the first to explicitly model agents’ latency preferences and their stochasticity. We propose Reverberation Transform—inspired by acoustic reverberation—featuring learnable dual-reverberation kernels that separately characterize asynchronous delay distributions in the perception-to-processing and processing-to-action stages, while end-to-end integrating spatiotemporal interaction cues. Our method achieves state-of-the-art accuracy on multiple pedestrian and vehicle trajectory forecasting benchmarks. Crucially, the learned latency parameters exhibit cross-scenario and cross-agent interpretability, revealing systematic response-dynamics differences across agent categories (e.g., pedestrians vs. vehicles). This advances a causally grounded paradigm for trajectory prediction.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Reasoning under Uncertainty: Sequential Decision Making

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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.

Problem

• Explicitly learning temporal delays in agent responses
• Modeling distinct latency preferences across different agents
• Addressing implausible trajectories caused by unconsidered latencies

Innovation

• Proposes reverberation transform for latency modeling
• Uses learnable kernels to predict agent latencies
• Enables controllable trajectory forecasting with latencies