Thesis Working Title

BAV - (Blended Driver/Autonomy Assessment using Telematics)​

Telematics describes a range of hardware devices installed in a vehicle, capable of capturing granular movements (acceleration/deceleration, turning force and velocity), GPS information and other general information relating to the vehicle (fuel capacity, fuel type). In addition, using a SIM card, a telematics device can send information periodically to a central source. Transmissible data includes road conditions, time of day, vehicle condition, acceleration/braking habits, rotational speed and velocity. This detailed driver information provided by telematics devices is conducive to insurers' modern risk calculation approaches.

Driver data recorded by a telematics device contains an individual's driving behaviour. Driving behaviour defines a series or single event in which a driver physically alters a vehicle state. For example, this change may be in acceleration, deceleration, cornering or velocity. Combining these alterations signifies a driver style unique to that driver. In essence, modelling or predicting this behaviour is invaluable for insurers, as insurers can deduce an individual's risk from their driving behaviour. Using the data from a telematics device, an insurer may offer bespoke insurance products such as pay-as/how-you-drive (PAYD/PHYD) products. These products monitor driver behaviours and offer dynamically priced premiums based on driver competency.

This research has a unique opportunity to analyse a naturalistic telematics dataset spread over a fleet of vehicles throughout Europe. Greenval, a private insurer, has agreed to supply and provide access to their telematics data. Access to this data allows for innovation in predicting driver risk, providing novel risk-pricing techniques, evaluating the efficiency of Advanced Driver Assistance Systems (ADAS) and the potential impact of widespread adoption of electric vehicles compared to traditional combustion vehicles.