Trajectory Prediction using Generative Adversarial Network in Multi-Class Scenarios
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Abstract (arXiv)
Predicting traffic agents' trajectories is an important task for auto-piloting. Most previous work on trajectory prediction only considers a single class of road agents. We use a sequence-to-sequence model to predict future paths from observed paths and we incorporate class information into the model by concatenating extracted label representations with traditional location inputs. We experiment with both LSTM and transformer encoders and we use generative adversarial network as introduced in Social GAN to learn the multi-modal behavior of traffic agents. We train our model on Stanford Drone dataset which includes 6 classes of road agents and evaluate the impact of different model components on the prediction performance in multi-class scenes.
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References · 10
- 2017Attention Is All You Needcited by 60,361
- 2014Conditional Generative Adversarial Netscited by 2,162
- 2018Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networkscited by 690
- 2021AgentFormer: Agent-Aware Transformers for Socio-Temporal Multi-Agent Forecastingcited by 331
- 2018SoPhie: An Attentive GAN for Predicting Paths Compliant to Social and Physical Constraintscited by 298
- 2020It Is Not the Journey but the Destination: Endpoint Conditioned Trajectory Predictioncited by 236
- 2019Social-BiGAT: Multimodal Trajectory Forecasting using Bicycle-GAN and Graph Attention Networkscited by 229
- 2020From Goals, Waypoints & Paths To Long Term Human Trajectory Forecastingcited by 167
- 2020MultiXNet: Multiclass Multistage Multimodal Motion Predictioncited by 31
- 2019Disentangling Human Dynamics for Pedestrian Locomotion Forecasting with Noisy Supervisioncited by 27
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