Model Predictive Control for Crowd Navigation via Learning-Based Trajectory Prediction

• URL: http://arxiv.org/abs/2508.07079v1
• Date: Sat, 09 Aug 2025 19:11:28 GMT
• Title: Model Predictive Control for Crowd Navigation via Learning-Based Trajectory Prediction
• Authors: Mohamed Parvez Aslam, Bojan Derajic, Mohamed-Khalil Bouzidi, Sebastian Bernhard, Jan Oliver Ringert,
• Abstract summary: 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.<n>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.<n>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.
• Score: 2.8544513613730214
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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