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.

提出了一种新的贝叶斯神经随机微分方程模型BNSP-SFM，将行为SDE模型与贝叶斯神经网络（BNNs）相结合，展示出了卓越的预测能力，并提供了行为和观察的可量化不确定性强的解释能力，实验证明BNSP-SFM的预测准确性比11种现有方法提高了50％，且对环境和人群密度等变化的适应能力较强。

可解释的人类轨迹预测带有行为不确定性