Bio-TAD: Biomechanical Target-Anchored Diffusion for Multimodal Pedestrian Trajectory Prediction

Intelligent systems in autonomous driving necessitate effective approaches for predicting multimodal pedestrian trajectories, especially during highly stochastic maneuvers characterized by minimal early macro-spatial displacement. We present Bio-TAD, an innovative backbone-agnostic generative framework that utilizes fine-grained biomechanical posture cues to effectively anchor a trajectory diffusion model. Instead of passively concatenating continuous features, our method employs an Orthogonal Prototype Memory Bank to convert kinematic signals into discrete behavioral primitives, thereby constraining the generative space. The generation process is regulated by a target-oriented Adaptive Layer Normalization (AdaLN) engine and optimized through a cohesive Multi-Task Diffusion Objective that aligns spatial diffusion errors and destination anchoring concurrently. Comprehensive assessments across three benchmark datasets demonstrate superior performance, yielding up to a 12% reduction in Final Displacement Error relative to the closest pose-augmented prior work, and an absolute improvement of up to 12.11% in semantic Intent Rate over coordinate-only baselines. Through controlled ablation, we further show that discretizing pose into orthogonally-regularized primitives outperforms continuous concatenation under otherwise identical conditions, isolating discretization as the source of the gain. To expose this effect, to which standard minADE/minFDE metrics are structurally blind, we introduce a novel Intent Rate metric that measures semantic mode correctness against a training-decoupled geometric classifier.