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Papers/The Garden of Forking Paths: Towards Multi-Future Trajecto...

The Garden of Forking Paths: Towards Multi-Future Trajectory Prediction

Junwei Liang, Lu Jiang, Kevin Murphy, Ting Yu, Alexander Hauptmann

2019-12-13CVPR 2020 6Trajectory ForecastingHuman motion predictionmotion predictionMulti Future Trajectory PredictionAutonomous DrivingMulti-future Trajectory PredictionSelf-Driving CarsTrajectory Prediction
PaperPDFCode(official)

Abstract

This paper studies the problem of predicting the distribution over multiple possible future paths of people as they move through various visual scenes. We make two main contributions. The first contribution is a new dataset, created in a realistic 3D simulator, which is based on real world trajectory data, and then extrapolated by human annotators to achieve different latent goals. This provides the first benchmark for quantitative evaluation of the models to predict multi-future trajectories. The second contribution is a new model to generate multiple plausible future trajectories, which contains novel designs of using multi-scale location encodings and convolutional RNNs over graphs. We refer to our model as Multiverse. We show that our model achieves the best results on our dataset, as well as on the real-world VIRAT/ActEV dataset (which just contains one possible future).

Results

TaskDatasetMetricValueModel
Trajectory PredictionStanford DroneADE-8/12 @K = 2014.78Multiverse
Trajectory PredictionStanford DroneFDE-8/12 @K= 2027.09Multiverse
Trajectory PredictionForkingPathsADE168.9Multiverse
Trajectory PredictionActEVADE-8/1218.51Multiverse
Trajectory PredictionActEVFDE-8/1235.84Multiverse
Trajectory PredictionForkingPathsADE168.9Multiverse

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