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Smartphone based estimation of relative risk propensity for inducing good driving behavior

Human activities analyses based on sensor data are gaining much importance. Of particular importance are those situations where man-machine interaction needs to be studied. The detection of risk induced while driving and customizing the insurance premium accordingly is an appropriate example. For such, the individuals are induced to utilize their personally owned smartphones in order to collect data and share them with insurance companies. The traditional approach counts the number of harsh events and infers the risk induced. However, such event-based inference is not necessarily a suitable approach for understanding each individual's propensity to indulge in high risk maneuvers. We propose an alternate method where a statistical route is adopted for quantifying risk propensity as well as a comparative analysis amongst a peer-group of drivers. A relatively moderate scale experimental test bed had been deployed by collecting driving related data (using their smartphones) from approximately 50 volunteers, fora duration of two months at a stretch. The experiment was unsupervised and the relative assessment is dependent on the quantity and quality of the collected data for each driver. In our model, the acceleration profiles displayed by each driver for every completed trip are observed to extract statistical features like 'Skewness squared' and 'Kurtosis'. It is observed that the kurtosis of the acceleration profiles stores major information about the driving styles. Subsequently, we have used statistical techniques to identify trends in data and used it to quantify the nature of the driving style. A comparative analysis within the peer-group (people with similar demographic features and similar work responsibilities) is done to judge individual propensities. It is envisaged that such application can be used to induce road safety through competitive spirit; additionally a large enterprise will find this tool useful to encourage employees to move towards a safe and fulfilling lifestyle. In this paper, we present the initial results of the above mentioned exercise using a smaller subset of the collected data.