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Driving risk prediction has been a topic of much research over the past fewdecades to minimize driving risk and increase safety. The use of demographicinformation in risk prediction is a traditional solution with applications ininsurance planning, however, it is difficult to capture true driving behaviorvia such coarse-grained factors. Therefor, the use of telematics data hasgained a widespread popularity over the past decade. While most of the existingstudies leverage demographic information in addition to telematics data, ourobjective is to maximize the use of telematics as well as contextualinformation (e.g., road-type) to build a risk prediction framework withreal-world applications. We contextualize telematics data in a variety offorms, and then use it to develop a risk classifier, assuming that there aresome weak risk labels available (e.g., past traffic citation records). Beforebuilding a risk classifier though, we employ a novel data-driven process toaugment weak risk labels. Extensive analysis and results based on real-worlddata from multiple major cities in the United States demonstrate usefulness ofthe proposed framework.

Judge Me in Context: A Telematics-Based Driving Risk Prediction Framework in Presence of Weak Risk Labels