Kinematic Single Vehicle Trajectory Prediction Baselines and Applications with the NGSIM Dataset
This work addresses a reproducibility gap for researchers in autonomous driving and traffic modeling, though it is incremental as it focuses on establishing baselines rather than introducing new methods.
The authors tackled the lack of reproducible baselines in vehicle trajectory prediction by implementing simple models (constant velocity and single-vehicle prediction) on the NGSIM dataset, reporting metrics like RMSE and FDE, and providing open code for replication.
In the recent vehicle trajectory prediction literature, the most common baselines are briefly introduced without the necessary information to reproduce it. In this article we produce reproducible vehicle prediction results from simple models. For that purpose, the process is explicit, and the code is available. Those baseline models are a constant velocity model and a single-vehicle prediction model. They are applied on the NGSIM US-101 and I-80 datasets using only relative positions. Thus, the process can be reproduced with any database containing tracking of vehicle positions. The evaluation reports Root Mean Squared Error (RMSE), Final Displacement Error (FDE), Negative Log-Likelihood (NLL), and Miss Rate (MR). The NLL estimation needs a careful definition because several formulations that differ from the mathematical definition are used in other works. This article is meant to be used along with the published code to establish baselines for further work. An extension is proposed to replace the constant velocity assumption with a learned model using a recurrent neural network. This brings good improvements in accuracy and uncertainty estimation and opens possibilities for both complex and interpretable models.