An Empirical Bayes Analysis of Vehicle Trajectory Models. (arXiv:2211.01696v1 [cs.LG])

We present an in-depth empirical analysis of the trade-off between model
complexity and representation error in modelling vehicle trajectories.
Analyzing several large public datasets, we show that simple linear models do
represent realworld trajectories with high fidelity over relevant time scales
at very moderate model complexity. This finding allows the formulation of
trajectory tracking and prediction as a Bayesian filtering problem. Using an
Empirical Bayes approach, we estimate prior distributions over model parameters
from the data that inform the motion …

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