Online Assessment of Driving Riskiness via Smartphone-Based Inertial Measurements

Assessing the driving-style from dynamic data is a well established line of research, which has tackled the description of risky behaviours, the profiling of energy-consumption habits, and the detection of different driver's characteristics from the analysis of motion data. In the last years, as smartphone ownership has become widespread, such an assessment has been increasingly relying on the measurements taken from the inertial sensors on board of the smartphone itself. This work stands in this context, and it aims to design a 4-dimensional driving-style assessment for insurance purposes. The main contribution is adding, to more common proxies of risky-driving, the dimension of smartphone usage, the detection of which is performed through an appropriate processing of smartphone-based inertial sensors, thus not relying on privacy-sensitive monitoring of phone usage behavior. Physics-based, fine-grained dynamic features are used to classify the overall riskiness of the driving-style, thus providing a comprehensive insight into the most discriminating features. The study is based on experimental data, collected over more than five thousands kilometers of varied car trips.

Automated summary

This study aims to design a 4-dimensional driving-style assessment system based on smartphone inertial sensor data. By processing dynamic features in detail, it is possible to comprehensively classify driving riskiness without relying on privacy-sensitive smartphone usage monitoring.