Multivariate Asynchronous Shapelets for Imbalanced Car Crash Predictions

Real-time vehicle safety and performance monitoring through crash data recorders is transforming mobility-related businesses. In this work, we collaborate with Generali Italia to improve their in-development automatic decision-making system, designed to assist operators in handling customer car crashes. Currently, Generali uses a deep learning model that can accurately alert operators of potential crashes, but its black-box nature can hinder the operator’s trustworthiness in the model. Given these limitations, we propose MARS, an interpretable shapelet-based classifier using novel multivariate asynchronous shapelets. We show that MARS can handle Generali’s highly irregular and imbalanced time series dataset, outperforming state-of-the-art classifiers and anomaly detection algorithms, including Generali’s black-box system. Further, we validate MARS on multivariate datasets from the UEA repository, demonstrating its competitiveness with existing techniques and providing examples of the explanations MARS can produce.