Human Motion Trajectory Prediction: A Survey

Abstract

With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. This paper provides a survey of human motion trajectory prediction. We review, analyze and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.

Submitted to the International Journal of Robotics Research (IJRR), 37 pages

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• A Survey on Socially Aware Robot Navigation: Taxonomy and Future Challenges
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• Dynamic Dense Graph Convolutional Network for Skeleton-based Human Motion Prediction
• From SLAM to Situational Awareness: Challenges and Survey
• OSDaR23: Open Sensor Data for Rail 2023
• Long-term Pedestrian Trajectory Prediction using Mutable Intention Filter and Warp LSTM
• AC-VRNN: Attentive Conditional-VRNN for Multi-Future Trajectory Prediction
• Data-driven Analysis for Understanding Team Sports Behaviors
• Trajectory Prediction for Autonomous Driving: Progress, Limitations, and Future Directions
• Predicting Like A Pilot: Dataset and Method to Predict Socially-Aware Aircraft Trajectories in Non-Towered Terminal Airspace
• Evaluating Pedestrian Trajectory Prediction Methods with Respect to Autonomous Driving
• Noticing Motion Patterns: Temporal CNN with a Novel Convolution Operator for Human Trajectory Prediction
• Learning Sparse Interaction Graphs of Partially Detected Pedestrians for Trajectory Prediction
• PRISMA: A Novel Approach for Deriving Probabilistic Surrogate Safety Measures for Risk Evaluation
• The Magni Human Motion Dataset: Accurate, Complex, Multi-Modal, Natural, Semantically-Rich and Contextualized
• Vulnerable Road User Detection and Safety Enhancement: A Comprehensive Survey
• Towards trustworthy multi-modal motion prediction: Holistic evaluation and interpretability of outputs
• Benchmark for Models Predicting Human Behavior in Gap Acceptance Scenarios
• ObserveNet Control: A Vision-Dynamics Learning Approach to Predictive Control in Autonomous Vehicles
• Framework for Network-Constrained Tracking of Cyclists and Pedestrians
• Workspace Optimization Techniques to Improve Prediction of Human Motion During Human-Robot Collaboration
• MissFormer: (In-)attention-based handling of missing observations for trajectory filtering and prediction
• Learning Structured Representations of Spatial and Interactive Dynamics for Trajectory Prediction in Crowded Scenes
• Hybrid Machine Learning Model with a Constrained Action Space for Trajectory Prediction
• Meta-path Analysis on Spatio-Temporal Graphs for Pedestrian Trajectory Prediction
• SHINE: Social Homology Identification for Navigation in Crowded Environments
• Context-free Self-Conditioned GAN for Trajectory Forecasting
• Characterizing Structured versus Unstructured Environments based on Pedestrians' and Vehicles' Motion Trajectories
• Handling Missing Observations with an RNN-based Prediction-Update Cycle
• Spatial and social situation-aware transformer-based trajectory prediction of autonomous systems
• Object Permanence Through Audio-Visual Representations
• A Comparative Study of Human Motion Models in Reinforcement Learning Algorithms for Social Robot Navigation
• HMPCC: Human-Aware Model Predictive Coverage Control
• Bayesian Floor Field: Transferring people flow predictions across environments
• Human motion trajectory prediction using the Social Force Model for real-time and low computational cost applications
• Long-Term Prediction of Local and Global Human Motion with Occlusion Recovery