MSES: Multi-Scale Temporal Encoding and Egocentric Scan-Based Spatial Representation for Multi-Agent Trajectory Prediction

Multi-agent trajectory prediction plays an increasingly critical role in intelligent transportation systems. Despite significant progress in this field, several key challenges remain unresolved. Future trajectories of agents are jointly influenced by individual behavior patterns and the surrounding environment, while most existing methods extract temporal features at a single time scale, limiting their capacity to capture complex temporal dependencies within trajectory sequences. Moreover, many current approaches employ numerically precise formulations for interaction modeling, which are misaligned with the imprecise nature of real-world social behavior. To address these limitations, we propose a multi-agent trajectory prediction model that combines multi-scale temporal encoding and egocentric scan-based spatial representation. Temporally, we leverage a sliding-window-based multi-scale temporal encoder to capture trajectory features across diverse time scales. Spatially, we partition the surrounding environment into multiple egocentric bins to represent social zones, thereby simulating real-world interaction patterns. Experimental results on public benchmark datasets ETH/UCY and SDD demonstrate that our model outperforms existing approaches.