Multi-Class Trajectory Prediction in Urban Traffic Using the View-of-Delft Prediction Dataset
H.J. Boekema (TU Delft - Intelligent Vehicles)
Bruno K.W. Martens (Student TU Delft)
J. F. P. Kooij (TU Delft - Intelligent Vehicles)
D. Gavrila (TU Delft - Intelligent Vehicles)
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Abstract
This letter presents View-of-Delft Prediction, a new dataset for trajectory prediction, to address the lack of on-board trajectory datasets in urban mixed-traffic environments. View-of-Delft Prediction builds on the recently released urban View-of-Delft (VoD) dataset to make it suitable for trajectory prediction. Unique features of this dataset are the challenging road layouts of Delft, with many narrow roads and bridges, and the close proximity between vehicles and Vulnerable Road Users (VRUs). It contains a large proportion of VRUs, with 569 prediction instances for vehicles, 347 for cyclists, and 934 for pedestrians. We additionally provide high-definition map annotations for the VoD dataset to enable state-of-the-art prediction models to be used. We analyse two state-of-the-art trajectory prediction models, PGP and P2T, which originally were developed for vehicle-dominated traffic scenarios, to assess the strengths and weaknesses of current modelling approaches in mixed traffic settings with large numbers of VRUs. Our analysis shows that there is a significant domain gap between the vehicle-dominated nuScenes and VRU-dominated VoD Prediction datasets. The dataset is publicly released for non-commercial research purposes.