Human Trajectory Forecasting with Explainable Behavioral Uncertainty
This work addresses the need for accurate and explainable trajectory predictions in applications like social robots and self-driving cars, representing an incremental advance by hybridizing existing approaches.
The paper tackles the problem of human trajectory forecasting by proposing a Bayesian Neural Stochastic Differential Equation model (BNSP-SFM) that combines model-free and model-based methods, achieving up to a 50% improvement in prediction accuracy compared to 11 state-of-the-art methods and better generalization to different scenes.
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.