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This paper focuses on the problem of predicting the future position of atarget road user given its current state, consisting of position and velocity.A weighted average approach is adopted, where the weights are determined fromdata containing the state trajectories of previously observed road users. Inparticular, a similarity function is introduced to extract from data thosepreviously observed road users' states that are most similar to the target'sone. This formulation results in an easily interpretable model with fewparameters to calibrate. The performance of this weighted average model(WAM) isevaluated on the same real-world data as state-of-the-art methods, showingpromising results. WAM outperforms the baseline constant velocity model atlonger prediction horizons, making WAM suitable for motion planningapplications. WAM and a baseline neural network model performs comparably.Still, WAM has only three parameters which are easily interpretable, while thecomplex neural network model has thousands of parameters which are difficult toanalyze.

Road User Position Prediction in Urban Environments via Locally Weighted Learning