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Related Experiment Video

Updated: Mar 10, 2026

Trajectory Data Analyses for Pedestrian Space-time Activity Study
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Towards Assessing the Human Trajectory Planning Horizon.

Daniel Carton1, Verena Nitsch2, Dominik Meinzer1

  • 1Chair of Automatic Control Engineering, Technical University of Munich, Theresienstr. 90, 80333 Munich, Germany.

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|December 10, 2016
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Summary

Humans shorten their trajectory planning horizon to avoid collisions with unexpected disturbances. This finding improves mobile robot navigation and human-robot interaction by enhancing locomotion prediction accuracy.

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Area of Science:

  • Robotics
  • Human-Robot Interaction
  • Cognitive Science

Background:

  • Mobile robots require seamless integration into human environments, necessitating accurate human locomotion prediction.
  • Existing models struggle to predict trajectories during sudden avoidance maneuvers caused by other agents.
  • Understanding human behavior in dynamic environments is crucial for safe and efficient human-robot collaboration.

Purpose of the Study:

  • To investigate if humans adjust their trajectory planning horizon to resolve emergent collision situations.
  • To model human locomotion as a model predictive controller to analyze planning horizon effects.
  • To experimentally validate changes in human locomotion planning behavior during disturbances.

Main Methods:

  • Simulations using a model predictive control (MPC) framework to model human behavior.
  • Investigating the influence of varying planning horizons on collision avoidance strategies.
  • Designing and conducting experiments in complex environments to observe human locomotion adjustments.

Main Results:

  • Simulations indicated that a shorter planning horizon aids in resolving abrupt collision situations.
  • Experimental results supported the hypothesis that humans reduce their planning horizon when facing unexpected disturbances.
  • Observed changes in locomotion planning behavior were linked to avoiding collisions.

Conclusions:

  • Humans dynamically adjust their planning horizon, employing shorter horizons for unexpected collision avoidance.
  • This research enhances the accuracy and generalizability of dynamic model-based prediction methods for human locomotion.
  • Findings contribute to more robust and intuitive human-robot interaction systems.