  Linear trajectory models provide mathematical advantages to autonomous
driving applications such as motion prediction. However, linear models'
expressive power and bias for real-world trajectories have not been thoroughly
analyzed. We present an in-depth empirical analysis of the trade-off between
model complexity and fit error in modelling object trajectories. We analyze
vehicle, cyclist, and pedestrian trajectories. Our methodology estimates
observation noise and prior distributions over model parameters from several
large-scale datasets. Incorporating these priors can then regularize prediction
models. Our results show that linear models do represent real-world
trajectories with high fidelity at very moderate model complexity. This
suggests the feasibility of using linear trajectory models in future motion
prediction systems with inherent mathematical advantages.

Machine Learning