RAPID: A Scalable and Controllable Physics-Informed Diffusion Framework for Real-Time Pedestrian Trajectory Generation

Abstract

Generating realistic and diverse pedestrian background flows is critical for numerous downstream applications, ranging from the training and validation of autonomous driving systems to the simulation of mobile communication networks. While recent diffusion-based models achieve state-of-the-art accuracy, they suffer from prohibitive inference latency, lack of physical consistency, and an inability to generalize across heterogeneous datasets, rendering them impractical for industrial Hardware-in-the-Loop testing. To address these challenges, we propose Real-time Adaptive Physics-Informed Diffusion (RAPID), a unified framework explicitly designed to balance high-fidelity generation with strict real-time constraints. First, we introduce a Canonical Representation Module that harmonizes diverse datasets via coordinate-invariant encoding and adaptive modality imputation, enabling unified training across varying scene scales. Second, we propose a Map Context Encoder that decouples computationally expensive map perception from the iterative denoising loop using cached latent embeddings. Third, a Physics-Informed Implicit Sampler integrates Social Force Model gradients as directional priors, encouraging physical consistency (e.g., collision avoidance). Extensive experiments on five heterogeneous benchmarks demonstrate that RAPID establishes a new state-of-the-art balance between fidelity and safety. Notably, it is the only framework capable of operating consistently below the 30 ms industrial threshold, maintaining almost constant inference latency regardless of crowd density. The system exhibits precise controllability over agent behaviors and has been successfully deployed in a production-grade autonomous-driving simulation platform as a core digital twin kernel for large-scale autonomous driving validation. The code is publicly available at https://github.com/tsinghua-fib-lab/RAPID.