Multiple Future Prediction Leveraging Synthetic Trajectories
| Autor: | Lorenzo Berlincioni, Alberto Del Bimbo, Federico Becattini, Lorenzo Seidenari |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Computer science Computer Vision and Pattern Recognition (cs.CV) 0211 other engineering and technologies Data Augmentation Autonomous Driving Trajectory Prediction Synthetic Data Machine Learning Computer Science - Computer Vision and Pattern Recognition Markov process 02 engineering and technology Machine learning computer.software_genre Synthetic data Trajectory Prediction Data-driven Data modeling Task (project management) Autonomous Driving Synthetic Data Machine Learning symbols.namesake Computer Science - Robotics Position (vector) Data Augmentation 0202 electrical engineering electronic engineering information engineering Markov chain business.industry 021107 urban & regional planning Trajectory symbols 020201 artificial intelligence & image processing Artificial intelligence business computer Robotics (cs.RO) |
| Zdroj: | 2020 25th International Conference on Pattern Recognition (ICPR) ICPR |
| Popis: | Trajectory prediction is an important task, especially in autonomous driving. The ability to forecast the position of other moving agents can yield to an effective planning, ensuring safety for the autonomous vehicle as well for the observed entities. In this work we propose a data driven approach based on Markov Chains to generate synthetic trajectories, which are useful for training a multiple future trajectory predictor. The advantages are twofold: on the one hand synthetic samples can be used to augment existing datasets and train more effective predictors; on the other hand, it allows to generate samples with multiple ground truths, corresponding to diverse equally likely outcomes of the observed trajectory. We define a trajectory prediction model and a loss that explicitly address the multimodality of the problem and we show that combining synthetic and real data leads to prediction improvements, obtaining state of the art results. Accepted at ICPR2020 |
| Databáze: | OpenAIRE |
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