Road User Specific Trajectory Prediction in Mixed Traffic Using Map Data

This paper studies road user trajectory prediction in mixed traffic, i.e. where vehicles and Vulnerable Road Users (VRUs, i.e. pedestrians, cyclists and other riders) closely share a common road space. We investigate if typical prediction components (scene graph representation, scene encoding, waypoint prediction, motion dynamics) should be specific to each road user class. Using the recent VRU-heavy View-of-Delft Prediction (VoD-P) dataset, we study several directions to improve the performance of the state-of-the-art map-based prediction models (PGP, TNT) in urban settings. First, we consider the use of class-specific map representations. Second, we investigate if the weights of different components of the model should be shared or separated by class. Finally, we augment VoD-P training data with automatically extracted trajectories from the 360-degree LiDAR scans by the recording vehicle. This data is made publicly available. We find that pre-training the model on auto-labels and making it class-specific leads to a reduction of up to 22.2%, 20.0%, and 18.2% in minADE ($K = 10$ samples) for pedestrians, cyclists, and vehicles, respectively.