MUSCLE-NET: Predicted-Multiscale-Aware Network for Pedestrian Trajectory Forecasting
For autonomous driving and intelligent transportation systems, this work improves pedestrian trajectory prediction by better handling scale-dependent motion dynamics, though gains are incremental over existing methods.
MUSCLE-NET addresses the problem of pedestrian trajectory forecasting by integrating multimodal cues (bounding boxes, velocities, pose) with scale-adaptive prediction mechanisms. On JAAD and PIE benchmarks, it achieves competitive performance and consistent gains over state-of-the-art methods.
Accurate pedestrian trajectory prediction is essential for safe navigation in autonomous driving and intelligent transportation systems. Despite substantial progress made by recent methods, most existing approaches are limited in fully exploiting diverse observations and often overlook the scale dependency of future motion, treating multiscale features uniformly regardless of underlying motion dynamics. This limits their robustness across diverse pedestrian behaviors. To address these challenges, we propose a Predicted-MUltiSCale-Aware Network (MUSCLE-NET) for Pedestrian Trajectory Forecasting that integrates complementary multimodal cues with scale-adaptive prediction mechanisms. The proposed framework is built upon a Multiscale Multimodal Feature Extraction (MMFE) module, which combines multiscale representation, modality-aware recalibration, and directional cross-modal fusion to construct semantically aligned representations from bounding boxes, velocities, and pose information. Building on these features, a Multiscale Enhanced Hierarchical Prediction (MEHP) module performs prediction-aware future-motion refinement via a probabilistic coarse predictor, scale-aligned fusion, and progressive refinement, adaptively selecting scale-relevant cues to mitigate spatial drift. Extensive experiments on the JAAD and PIE benchmarks demonstrate that the proposed MUSCLE-Net achieves competitive performance and consistent gains compared with state-of-the-art trajectory prediction methods.