Deep Stochastic Kinematic Models for Probabilistic Motion Forecasting in Traffic
arxiv(2024)
摘要
Kinematic priors have shown to be helpful in boosting generalization and
performance in prior work on trajectory forecasting. Specifically, kinematic
priors have been applied such that models predict a set of actions instead of
future output trajectories. By unrolling predicted trajectories via time
integration and models of kinematic dynamics, predicted trajectories are not
only kinematically feasible on average but also relate uncertainty from one
timestep to the next. With benchmarks supporting prediction of multiple
trajectory predictions, deterministic kinematic priors are less and less
applicable to current models. We propose a method for integrating probabilistic
kinematic priors into modern probabilistic trajectory forecasting
architectures. The primary difference between our work and previous techniques
is the analytical quantification of variance, or uncertainty, in predicted
trajectories. With negligible additional computational overhead, our method can
be generalized and easily implemented with any modern probabilistic method that
models candidate trajectories as Gaussian distributions. In particular, our
method works especially well in unoptimal settings, such as with small datasets
or in the presence of noise. Our method achieves up to a 50
in small dataset settings and up to an 8
learning compared to previous kinematic prediction methods on SOTA trajectory
forecasting architectures out-of-the-box, with minimal fine-tuning. In this
paper, we show four analytical formulations of probabilistic kinematic priors
which can be used for any Gaussian Mixture Model (GMM)-based deep learning
models, quantify the error bound on linear approximations applied during
trajectory unrolling, and show results to evaluate each formulation in
trajectory forecasting.
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