Human Trajectory Forecasting with Explainable Behavioral Uncertainty
Jiangbei Yue Dinesh Manocha He Wang
Human trajectory forecasting helps to understand and predict human behaviors,
enabling applications from social robots to self-driving cars, and therefore
has been heavily investigated. Most existing methods can be divided into
model-free and model-based methods. Model-free methods offer superior
prediction accuracy but lack explainability, while model-based methods provide
explainability but cannot predict well. Combining both methodologies, we
propose a new Bayesian Neural Stochastic Differential Equation model BNSP-SFM,
where a behavior SDE model is combined with Bayesian neural networks (BNNs).
While the NNs provide superior predictive power, the SDE offers strong
explainability with quantifiable uncertainty in behavior and observation. We
show that BNSP-SFM achieves up to a 50% improvement in prediction accuracy,
compared with 11 state-of-the-art methods. BNSP-SFM also generalizes better to
drastically different scenes with different environments and crowd densities (~
20 times higher than the testing data). Finally, BNSP-SFM can provide
predictions with confidence to better explain potential causes of behaviors.
The code will be released upon acceptance.