How Am I Driving? Using Genetic Programming to Generate Scoring Functions for Urban Driving Behavior

Created by W.Langdon from gp-bibliography.bib Revision:1.8120

@Article{Lopez:2018:MCA,

• author = "Roberto Lopez and Luis Carlos {Gonzalez Gurrola} and Leonardo Trujillo and Olanda Prieto and Graciela Ramirez and Antonio Posada and Perla Juarez-Smith and Leticia Mendez",
• title = "How Am I Driving? Using Genetic Programming to Generate Scoring Functions for Urban Driving Behavior",
• journal = "Mathematical and Computational Applications",
• year = "2018",
• volume = "23",
• number = "2",
• pages = "19",
• month = jun,
• note = "Special Issue Numerical and Evolutionary Optimization",
• keywords = "genetic algorithms, genetic programming, driving scoring functions, driving events, risky driving, intelligent transportation systems",
• ISSN = "2297-8747",
• URL = "https://www.mdpi.com/2297-8747/23/2",
• URL = "https://www.mdpi.com/2297-8747/23/2/19/htm",
• URL = "https://www.mdpi.com/2297-8747/23/2/19/pdf/mca-23-00019.pdf",
• DOI = "doi:10.3390/mca23020019",
• size = "13 pages",
• abstract = "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 3percent 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 behaviour 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 manoeuvres, 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.",
• notes = "journal MCA has no real page numbers",

}

Genetic Programming entries for Jesus Roberto Lopez Santillan Luis Carlos Gonzalez Gurrola Leonardo Trujillo Olanda Prieto-Ordaz Graciela Maria de Jesus Ramirez Alonso Antonio Posada Perla Sarahi Juarez-Smith Norma Leticia Méndez Mariscal

Citations