# Convolutional Neural Network for Trajectory Prediction

arXiv: https://arxiv.org/abs/1809.00696 · HTML: https://mlsanity.com/paper/1809.00696/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Sep 2018.

## Built on by (most cited first, 11 of 11)

- [Review of Pedestrian Trajectory Prediction Methods: Comparing Deep Learning and Knowledge-based Approaches](https://mlsanity.com/paper/2111.06740/) (2021)
- [FloMo: Tractable Motion Prediction with Normalizing Flows](https://mlsanity.com/paper/2103.03614/) (2021)
- [Uncertainty estimation of pedestrian future trajectory using Bayesian approximation](https://mlsanity.com/paper/2205.01887/) (2022)
- [Aware of the History: Trajectory Forecasting with the Local Behavior Data](https://mlsanity.com/paper/2207.09646/) (2022)
- [Multiple Object Tracking with Mixture Density Networks for Trajectory Estimation](https://mlsanity.com/paper/2106.10950/) (2021)
- [Asymmetrical Bi-RNN for pedestrian trajectory encoding](https://mlsanity.com/paper/2106.04419/) (2021)
- [Generating Synthetic Training Data for Deep Learning-Based UAV Trajectory Prediction](https://mlsanity.com/paper/2107.00422/) (2021)
- [Handling Missing Observations with an RNN-based Prediction-Update Cycle](https://mlsanity.com/paper/2103.11747/) (2021)
- [MissFormer: (In-)attention-based handling of missing observations for trajectory filtering and prediction](https://mlsanity.com/paper/2106.16009/) (2021)
- [Spatio-Temporal Look-Ahead Trajectory Prediction using Memory Neural Network](https://mlsanity.com/paper/2102.12070/) (2021)
- [Movement Analytics: Current Status, Application to Manufacturing, and Future Prospects from an AI Perspective](https://mlsanity.com/paper/2210.01344/) (2022)

## Abstract (arXiv)

Predicting trajectories of pedestrians is quintessential for autonomous robots which share the same environment with humans. In order to effectively and safely interact with humans, trajectory prediction needs to be both precise and computationally efficient. In this work, we propose a convolutional neural network (CNN) based human trajectory prediction approach. Unlike more recent LSTM-based moles which attend sequentially to each frame, our model supports increased parallelism and effective temporal representation. The proposed compact CNN model is faster than the current approaches yet still yields competitive results.