Predictive Monitoring of Traffic Rules

Predicting the trajectories of other road users relies to a large extent on the assumption that they adhere to the legally binding traffic rules. Hence, when this assumption does not hold anymore, the prediction becomes invalid, putting autonomous vehicles relying on such predictions in a critical situation. We propose a solution to this problem by predicting traffic rule violations. All traffic rules are modeled by temporal logic, and we provide real-valued generalizations of required logical predicates to obtain features for prediction with neural networks. The usefulness of our approach is demonstrated by predicting rule violations on a dataset recorded from a highway. Our results show that directly learning traffic rule violations using the features from temporal logic formulas often performs better compared to separately predicting and monitoring trajectories.