LAUSR

According to the World Health Organization, recent years have seen a dramatic increase in the number of car accidents worldwide. In an attempt to ameliorate this situation, the automotive and telematics industry has tried to develop technology that can help the drivers make better and safer decisions. One approach is to develop systems that give feedback to the driver by means of a driving score, so that the driver can analyze his driving habits. By considering sensing platforms embedded into either vehicles or smartphones, this paper models the driving score calculation task as a regression problem. Accordingly, we propose novel scoring functions that are generated through a metaheuristic for automatic program induction called genetic programming (GP). In addition to our proposal, we evaluate six other computational methods, three of which are based on works reported in the literature. Results show that the functions generated by the GP clearly outperform all studied competitors. Moreover, the functions are simple enough to be transferred to an on-board vehicle computer or to the smartphone of the driver. Given the white-box nature of the proposed scoring solutions, these could be implemented in the aforementioned sensing platforms by interested parties to offer better insurance plans, as a tool for parents to monitor young drivers, or by users of public or private transportation system to evaluate and report their experience. Presented results indicate that the average velocity is the most important variable for driver scoring.