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In the recent vehicle trajectory prediction literature, the most commonbaselines are briefly introduced without the necessary information to reproduceit. In this article we produce reproducible vehicle prediction results fromsimple models. For that purpose, the process is explicit, and the code isavailable. Those baseline models are a constant velocity model and asingle-vehicle prediction model. They are applied on the NGSIM US-101 and I-80datasets using only relative positions. Thus, the process can be reproducedwith any database containing tracking of vehicle positions. The evaluationreports Root Mean Squared Error (RMSE), Final Displacement Error (FDE),Negative Log-Likelihood (NLL), and Miss Rate (MR). The NLL estimation needs acareful definition because several formulations that differ from themathematical definition are used in other works. This article is meant to beused along with the published code to establish baselines for further work. Anextension is proposed to replace the constant velocity assumption with alearned model using a recurrent neural network. This brings good improvementsin accuracy and uncertainty estimation and opens possibilities for both complexand interpretable models.

Kinematic Single Vehicle Trajectory Prediction Baselines and Applications with the NGSIM Dataset