Driving Profile using Evolutionary Computation



Road injuries are among the top ten causes of death worldwide. It has been shown that providing feedback to drivers decreases the likeliness of having them engaging into dangerous manoeuvres, such as speeding. It also contributes to reduce the amount of life-threatening incidents related with braking. Due to its ubiquity, smartphones are a great resource for assessing driving behaviour. Several mobile applications have been created with this purpose, but there is no concrete evidence that these approaches offer consistent results over distinct platforms (Operating Systems) and hardware. Providing a model for assessing driver behaviour across distinct devices represents a major challenge, due to the increasing differentiation between platforms and mobile devices' internal sensors (gyroscope, accelerometer, GPS, and magnetometer.) In this study we propose the application of Evolutionary Computation techniques to create models for driving behaviour characterisation over data acquired from mobile devices with distinct sensors. Our experiments show that we are able to evolve models that are robust and can accurately identify the legs of a car journey that have abnormal events. In concrete we are able to evolve predictive models that can successfully create a profile about the driving behaviour of a person.


Smart phones , Sensors , Data models , Automobiles , Roads , Accelerometers


Evolutionary computation, machine learning, driver behaviour and safety


2019 IEEE Congress on Evolutionary Computation (CEC), August 2019


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