Safe navigation in pedestrian-rich environments remains a key challenge for autonomous robots. This work evaluates the integration of a deep learning-based Social-Implicit (SI) pedestrian trajectory predictor within a Model Predictive Control (MPC) framework on the physical Continental Corriere robot. Tested across varied pedestrian densities, the SI-MPC system is compared to a traditional Constant Velocity (CV) model in both open-loop prediction and closed-loop navigation. Results show that SI improves trajectory prediction - reducing errors by up to 76% in low-density settings - and enhances safety and motion smoothness in crowded scenes. Moreover, real-world deployment reveals discrepancies between open-loop metrics and closed-loop performance, as the SI model yields broader, more cautious predictions. These findings emphasize the importance of system-level evaluation and highlight the SI-MPC framework's promise for safer, more adaptive navigation in dynamic, human-populated environments.
@article{arxiv.2508.07079,
title = {Model Predictive Control for Crowd Navigation via Learning-Based Trajectory Prediction},
author = {Mohamed Parvez Aslam and Bojan Derajic and Mohamed-Khalil Bouzidi and Sebastian Bernhard and Jan Oliver Ringert},
journal= {arXiv preprint arXiv:2508.07079},
year = {2025}
}