How Am I Driving? Using Genetic Programming to Generate Scoring Functions for Urban Driving Behavior
Facultad de Ingeniería, Universidad Autónoma de Chihuahua, Circuito No. 1, Nuevo Campus Universitario, Apdo. postal 1552, Chihuahua 31240, Mexico
Departamento de Ingeniería en Electrónica y Eléctrica, Instituto Tecnológico de Tijuana, Calzada Tecnológico SN, Tomas Aquino, Tijuana 22414, Mexico
Author to whom correspondence should be addressed.
Math. Comput. Appl. 2018, 23(2), 19; https://doi.org/10.3390/mca23020019
Received: 7 March 2018 / Revised: 27 March 2018 / Accepted: 30 March 2018 / Published: 3 April 2018
(This article belongs to the Special Issue Numerical and Evolutionary Optimization)
Road traffic injuries are a serious concern in emerging economies. Their death toll and economic impact are shocking, with 9 out of 10 deaths occurring in low or middle-income countries; and road traffic crashes representing 3% of their gross domestic product. One way to mitigate these issues is to develop technology to effectively assist the driver, perhaps making him more aware about how her (his) decisions influence safety. Following this idea, in this paper we evaluate computational models that can score the behavior of a driver based on a risky-safety scale. Potential applications of these models include car rental agencies, insurance companies or transportation service providers. In a previous work, we showed that Genetic Programming (GP) was a successful methodology to evolve mathematical functions with the ability to learn how people subjectively score a road trip. The input to this model was a vector of frequencies of risky maneuvers, which were supposed to be detected in a sensor layer. Moreover, GP was shown, even with statistical significance, to be better than six other Machine Learning strategies, including Neural Networks, Support Vector Regression and a Fuzzy Inference system, among others. A pending task, since then, was to evaluate if a more detailed comparison of different strategies based on GP could improve upon the best GP model. In this work, we evaluate, side by side, scoring functions evolved by three different variants of GP. In the end, the results suggest that two of these strategies are very competitive in terms of accuracy and simplicity, both generating models that could be implemented in current technology that seeks to assist the driver in real-world scenarios.