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.

翻译：人类轨迹预测有助于理解和预测人类行为，从而支持从社交机器人到自动驾驶汽车等应用，因此得到了广泛研究。现有方法主要分为无模型方法和基于模型方法两类。无模型方法预测精度高但缺乏可解释性，而基于模型方法具有可解释性但预测效果不佳。结合两种方法的优势，我们提出了一种新的贝叶斯神经随机微分方程模型BNSP-SFM，该模型将行为SDE模型与贝叶斯神经网络（BNNs）相结合。其中，神经网络提供强大的预测能力，而SDE通过行为和观测中的可量化不确定性提供强可解释性。实验表明，与11种最新方法相比，BNSP-SFM的预测精度提升高达50%。该模型还能更好地泛化到环境与人群密度差异显著（测试数据密度约高20倍）的不同场景。最后，BNSP-SFM可提供带有置信度的预测，从而更好地解释行为的潜在原因。代码将在论文接收后开源。