mlsanityData to 14 Oct 2026

Review of Pedestrian Trajectory Prediction Methods: Comparing Deep Learning and Knowledge-based Approaches

arXiv 2111.06740 ↗First posted 11 Nov 2021cs.LG physics.soc-ph stat.MLcited by 30

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Abstract (arXiv)

In crowd scenarios, predicting trajectories of pedestrians is a complex and challenging task depending on many external factors. The topology of the scene and the interactions between the pedestrians are just some of them. Due to advancements in data-science and data collection technologies deep learning methods have recently become a research hotspot in numerous domains. Therefore, it is not surprising that more and more researchers apply these methods to predict trajectories of pedestrians. This paper compares these relatively new deep learning algorithms with classical knowledge-based models that are widely used to simulate pedestrian dynamics. It provides a comprehensive literature review of both approaches, explores technical and application oriented differences, and addresses open questions as well as future development directions. Our investigations point out that the pertinence of knowledge-based models to predict local trajectories is nowadays questionable because of the high accuracy of the deep learning algorithms. Nevertheless, the ability of deep-learning algorithms for large-scale simulation and the description of collective dynamics remains to be demonstrated. Furthermore, the comparison shows that the combination of both approaches (the hybrid approach) seems to be promising to overcome disadvantages like the missing explainability of the deep learning approach.

Key facts. Cited by 30 papers in the map (data through 14 Oct 2026). Builds on 76 of its 290 references that are in the map. First posted on arXiv 11 Nov 2021.

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76 of 290 references.

References · 76

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 4; 2024: 9; 2025: 13; 2026: 3.

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  1. 2022Behavioral Intention Prediction in Driving Scenes: A Surveycited by 30
  2. 2025Trajectory Prediction for Autonomous Driving: Progress, Limitations, and Future Directionscited by 14
  3. 2024Social Physics Informed Diffusion Model for Crowd Simulationcited by 11
  4. 2024Risk-Aware Vehicle Trajectory Prediction Under Safety-Critical Scenarioscited by 7
  5. 2024Vulnerable Road User Detection and Safety Enhancement: A Comprehensive Surveycited by 5
  6. 2025Unified Human Localization and Trajectory Prediction with Monocular Visioncited by 5
  7. 2025TrajEvo: Designing Trajectory Prediction Heuristics via LLM-driven Evolutioncited by 5
  8. 2025TrajEvo: Trajectory Prediction Heuristics Design via LLM-driven Evolutioncited by 5
  9. 2023Learning Pedestrian Actions to Ensure Safe Autonomous Drivingcited by 4
  10. 2024Understanding complex crowd dynamics with generative neural simulatorscited by 4
  11. 2023Local and Global Contextual Features Fusion for Pedestrian Intention Predictioncited by 3
  12. 2024Snapshot: Towards Application-centered Models for Pedestrian Trajectory Prediction in Urban Traffic Environmentscited by 3
18 more
  1. 2025Residual Neural Terminal Constraint for MPC-based Collision Avoidance in Dynamic Environmentscited by 3
  2. 2024Enhanced Prediction of Multi-Agent Trajectories via Control Inference and State-Space Dynamicscited by 2
  3. 2024TAB-Fields: A Maximum Entropy Framework for Mission-Aware Adversarial Planningcited by 2
  4. 2025TRACE: A Self-Improving Framework for Robot Behavior Forecasting with Vision-Language Modelscited by 2
  5. 2025Pedestrian-Aware Motion Planning for Autonomous Driving in Complex Urban Scenarioscited by 2
  6. 2025Recent Advances in Multi-Agent Human Trajectory Prediction: A Comprehensive Reviewcited by 2
  7. 2025TrajGATFormer: A Graph-Based Transformer Approach for Worker and Obstacle Trajectory Prediction in Off-site Construction Environmentscited by 2
  8. 2023Video Killed the HD-Map: Predicting Multi-Agent Behavior Directly From Aerial Imagescited by 1
  9. 2024Geometric Graph Neural Network Modeling of Human Interactions in Crowded Environmentscited by 1
  10. 2024Stochastic Reconstruction of Gappy Lagrangian Turbulent Signals by Conditional Diffusion Modelscited by 1
  11. 2023Ranking-based Adaptive Query Generation for DETRs in Crowded Pedestrian Detectioncited by 0
  12. 2025Prediction of Usage Probabilities of Shopping-Mall Corridors Using Heterogeneous Graph Neural Networkscited by 0
  13. 2025Model Predictive Control for Crowd Navigation via Learning-Based Trajectory Predictioncited by 0
  14. 2025Great GATsBi: Hybrid, Multimodal, Trajectory Forecasting for Bicycles using Anticipation Mechanismcited by 0
  15. 2025Hazy Pedestrian Trajectory Prediction via Physical Priors and Graph-Mambacited by 0
  16. 2026Modeling Vehicle-Type-Specific Pedestrian Crash Avoidance Behavior in Safety-Critical Interactions Using Smooth-Mamba Deep Reinforcement Learningcited by 0
  17. 2026Modeling Fly Social Behavior with Agent-Centric Pose Forecastingcited by 0
  18. 2026Safety-oriented pedestrian trajectory prediction at urban intersections using time-to-collision and crossing-zone contextcited by 0