# Pedestrian Trajectory Prediction with Convolutional Neural Networks

arXiv: https://arxiv.org/abs/2010.05796 · HTML: https://mlsanity.com/paper/2010.05796/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Oct 2020.

## Built on by (most cited first, 21 of 21)

- [SocialVAE: Human Trajectory Prediction using Timewise Latents](https://mlsanity.com/paper/2203.08207/) (2022)
- [Review of Pedestrian Trajectory Prediction Methods: Comparing Deep Learning and Knowledge-based Approaches](https://mlsanity.com/paper/2111.06740/) (2021)
- [CSCNet: Contextual Semantic Consistency Network for Trajectory Prediction in Crowded Spaces](https://mlsanity.com/paper/2202.08506/) (2022)
- [Trajectory Prediction for Autonomous Driving: Progress, Limitations, and Future Directions](https://mlsanity.com/paper/2503.03262/) (2025)
- [Uncertainty estimation of pedestrian future trajectory using Bayesian approximation](https://mlsanity.com/paper/2205.01887/) (2022)
- [Joint Pedestrian Trajectory Prediction through Posterior Sampling](https://mlsanity.com/paper/2404.00237/) (2024)
- [Under the Hood of Transformer Networks for Trajectory Forecasting](https://mlsanity.com/paper/2203.11878/) (2022)
- [Certified Human Trajectory Prediction](https://mlsanity.com/paper/2403.13778/) (2024)
- [Sparse Pedestrian Character Learning for Trajectory Prediction](https://mlsanity.com/paper/2311.15512/) (2023)
- [Snapshot: Towards Application-centered Models for Pedestrian Trajectory Prediction in Urban Traffic Environments](https://mlsanity.com/paper/2409.01971/) (2024)
- [VECTOR: Velocity-Enhanced GRU Neural Network for Real-Time 3D UAV Trajectory Prediction](https://mlsanity.com/paper/2410.23305/) (2024)
- [ASTRA: A Scene-aware TRAnsformer-based model for trajectory prediction](https://mlsanity.com/paper/2501.09878/) (2025)
- [Next Generation Equation-Free Multiscale Modelling of Crowd Dynamics via Machine Learning](https://mlsanity.com/paper/2508.03926/) (2025)
- [TPAD: Identifying Effective Trajectory Predictions Under the Guidance of Trajectory Anomaly Detection Model](https://mlsanity.com/paper/2201.02941/) (2022)
- [Robustness Benchmark of Road User Trajectory Prediction Models for Automated Driving](https://mlsanity.com/paper/2304.01895/) (2023)
- [Where Do You Go? Pedestrian Trajectory Prediction using Scene Features](https://mlsanity.com/paper/2501.13848/) (2025)
- [Towards Safe and Reliable Autonomous Driving: Dynamic Occupancy Set Prediction](https://mlsanity.com/paper/2402.19385/) (2024)
- [A low complexity contextual stacked ensemble-learning approach for pedestrian intent prediction](https://mlsanity.com/paper/2410.13039/) (2024)
- [MMD-OPT : Maximum Mean Discrepancy Based Sample Efficient Collision Risk Minimization for Autonomous Driving](https://mlsanity.com/paper/2412.09121/) (2024)
- [A Time-Enhanced Data Disentanglement Network for Traffic Flow Forecasting](https://mlsanity.com/paper/2506.02609/) (2025)
- [Unified Vision-Centric Pedestrian Crossing Action Prediction via Adaptive Patch Projection and Proactive Spatial Rectification](https://mlsanity.com/paper/2609.07420/) (2026)

## Abstract (arXiv)

Predicting the future trajectories of pedestrians is a challenging problem that has a range of application, from crowd surveillance to autonomous driving. In literature, methods to approach pedestrian trajectory prediction have evolved, transitioning from physics-based models to data-driven models based on recurrent neural networks. In this work, we propose a new approach to pedestrian trajectory prediction, with the introduction of a novel 2D convolutional model. This new model outperforms recurrent models, and it achieves state-of-the-art results on the ETH and TrajNet datasets. We also present an effective system to represent pedestrian positions and powerful data augmentation techniques, such as the addition of Gaussian noise and the use of random rotations, which can be applied to any model. As an additional exploratory analysis, we present experimental results on the inclusion of occupancy methods to model social information, which empirically show that these methods are ineffective in capturing social interaction.