Abstract
Pedestrian movement prediction is essential for autonomous mobile robots (AMR) and social robots that share the same environment with humans. A key challenge is the accurate prediction of pedestrian trajectories, due to uncertainties from environment, sensor factors, and unpredictable pedestrian behaviors. In this study, we propose a dual approach to enhance prediction accuracy by managing these uncertainties. First, we employ Monte Carlo Dropout in our predictive model to produce multiple prediction outputs. This method considers uncertainty in pedestrian movements, addressing the inherent unpredictability in human behavior. Second, we incorporate pedestrian pose information, using a posture estimation model with data from a first-person RGB-D camera. This integration provides critical cues for prediction, improving the model’s performance in Average Displacement Error (ADE) and FDE metrics. In the Future, we validate our model in more complex scenarios and real-world applications.
