Convolutional Neural Network for Trajectory Prediction
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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.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Sep 2018.
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Citing papers per year: 2021: 8; 2022: 3.
11 papers.
- 2021Review of Pedestrian Trajectory Prediction Methods: Comparing Deep Learning and Knowledge-based Approachescited by 30
- 2021FloMo: Tractable Motion Prediction with Normalizing Flowscited by 21
- 2022Uncertainty estimation of pedestrian future trajectory using Bayesian approximationcited by 12
- 2021Multiple Object Tracking with Mixture Density Networks for Trajectory Estimationcited by 8
- 2022Aware of the History: Trajectory Forecasting with the Local Behavior Datacited by 8
- 2021Asymmetrical Bi-RNN for pedestrian trajectory encodingcited by 6
- 2021Generating Synthetic Training Data for Deep Learning-Based UAV Trajectory Predictioncited by 3
- 2021Handling Missing Observations with an RNN-based Prediction-Update Cyclecited by 2
- 2021MissFormer: (In-)attention-based handling of missing observations for trajectory filtering and predictioncited by 2
- 2021Spatio-Temporal Look-Ahead Trajectory Prediction using Memory Neural Networkcited by 1
- 2022Movement Analytics: Current Status, Application to Manufacturing, and Future Prospects from an AI Perspectivecited by 1
- 2022Movement Analytics: Current Status, Application to Manufacturing, and Future Prospects from an AI Perspective4 Oct 2022
- 2022Aware of the History: Trajectory Forecasting with the Local Behavior Data20 Jul 2022
- 2022Uncertainty estimation of pedestrian future trajectory using Bayesian approximation4 May 2022
- 2021Review of Pedestrian Trajectory Prediction Methods: Comparing Deep Learning and Knowledge-based Approaches11 Nov 2021
- 2021Generating Synthetic Training Data for Deep Learning-Based UAV Trajectory Prediction1 Jul 2021
- 2021MissFormer: (In-)attention-based handling of missing observations for trajectory filtering and prediction30 Jun 2021
- 2021Multiple Object Tracking with Mixture Density Networks for Trajectory Estimation21 Jun 2021
- 2021Asymmetrical Bi-RNN for pedestrian trajectory encoding1 Jun 2021
- 2021Handling Missing Observations with an RNN-based Prediction-Update Cycle22 Mar 2021
- 2021FloMo: Tractable Motion Prediction with Normalizing Flows5 Mar 2021
- 2021Spatio-Temporal Look-Ahead Trajectory Prediction using Memory Neural Network24 Feb 2021