NeSyMoF: A Neuro-Symbolic Model for Motion Forecasting

Recent advancements in deep learning have significantly enhanced the development of efficient models for multi-modal path prediction within urban environments, offering approaches to navigate complex environments accurately. Despite their performance, models grounded in deep learning techniques frequently encounter challenges related to interpretability. This limitation not only hampers their practical application but also complicates the process of diagnosing and rectifying errors within these systems, which is a critical factor for ensuring reliability and safety in realworld deployments. In this paper we propose NeSyMoF, a Neuro-Symbolic model for Motion Forecasting, to address this critical gap by combining the predictive power of deep neural networks with the interpretable logic inherent in symbolic reasoning. Data processing in NeSyMoF involves extracting pertinent features from the agent’s environment and channeling them into a neuro-symbolic reasoning module. The neurosymbolic reasoning module generates first-order logic rules that describe and condition the path prediction process, thereby providing clear explanations and intentions behind the forecasts of the model. We evaluate our model with the Argoverse benchmark for path forecasting, as it includes challenging driving situations, necessary to extensively evaluate our model. The results of our evaluation show that NeSyMoF outperforms state-of-the-art interpretable models for single-mode predictions while providing logic-based explanations for its forecasts, that articulate the reasoning behind predictions, making NeSyMoF more adapted for human-centric applications.