Driver Insurance Premium Calculation using Advanced Driver Assistance Systems and Contextual Information: dataset

Published: 24 June 2024| Version 1 | DOI: 10.17632/zf5zv7d8rs.1


The dataset comprises driving data collected from an Irish fleet of 354 commercial drivers using light-good vehicles in a driving monitoring campaign. As part of their daily job activities, the drivers performed, on average, five daily trips, covering around 143 km. The data collection occurred in the Republic of Ireland between April 2021 and April 2022, encompassing 8,142,896 km from 287,511 trips, where drivers were monitored for 35 weeks on average. The fleet was monitored with telematics tracking devices and warning-based ADAS that triggered alarms about distraction-related events. With this information, all drivers received feedback about their driving patterns and attended quarterly coaching sessions to meet the fleet’s standards regarding road safety. Driving behaviour patterns are often constrained by the driving environment. Thus, geolocation data was used to retrieve the context of all daily trips. This contextualization was performed using the Motion-S contextualization service based on HERE Technologies's Platform Data Extension. As a result, this dataset contains weekly aggregated driving data including behavioural patterns, distraction-related events through ADAS warnings, and the driving context. The dataset also contains information about two years of the fleet claims history (2020 and 2021). In total, 62 at-fault claims were recorded, giving a mean cost per claim of €2,899, where 50 drivers had one claim and six drivers had two claims. Files: (txt file with description and variables) dataset_description.ipynb (Python notebook) weekly_driving_profiles.csv (data)



University of Limerick


Insurance, Autonomous Driving, Road Transportation