How Am I Driving? Using Genetic Programming to Generate Scoring Functions for Urban Driving Behavior
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @Article{Lopez:2018:MCA,
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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",
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title = "How Am I Driving? Using Genetic Programming to
Generate Scoring Functions for Urban Driving Behavior",
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journal = "Mathematical and Computational Applications",
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year = "2018",
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volume = "23",
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number = "2",
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pages = "19",
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month = jun,
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note = "Special Issue Numerical and Evolutionary
Optimization",
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keywords = "genetic algorithms, genetic programming, driving
scoring functions, driving events, risky driving,
intelligent transportation systems",
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ISSN = "2297-8747",
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URL = "https://www.mdpi.com/2297-8747/23/2",
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URL = "https://www.mdpi.com/2297-8747/23/2/19/htm",
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URL = "https://www.mdpi.com/2297-8747/23/2/19/pdf/mca-23-00019.pdf",
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DOI = "doi:10.3390/mca23020019",
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size = "13 pages",
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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.",
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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