An autonomous vehicle sequencing problem at intersections: A genetic algorithm approach

Fei Yan; Mahjoub Dridi; Abdellah El Moudni

International Journal of Applied Mathematics and Computer Science (2013)

  • Volume: 23, Issue: 1, page 183-200
  • ISSN: 1641-876X

Abstract

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This paper addresses a vehicle sequencing problem for adjacent intersections under the framework of Autonomous Intersection Management (AIM). In the context of AIM, autonomous vehicles are considered to be independent individuals and the traffic control aims at deciding on an efficient vehicle passing sequence. Since there are considerable vehicle passing combinations, how to find an efficient vehicle passing sequence in a short time becomes a big challenge, especially for more than one intersection. In this paper, we present a technique for combining certain vehicles into some basic groups with reference to some properties discussed in our earlier works. A genetic algorithm based on these basic groups is designed to find an optimal or a near-optimal vehicle passing sequence for each intersection. Computational experiments verify that the proposed genetic algorithms can response quickly for several intersections. Simulations with continuous vehicles are carried out with application of the proposed algorithm or existing traffic control methods. The results show that the traffic condition can be significantly improved by our algorithm.

How to cite

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Fei Yan, Mahjoub Dridi, and Abdellah El Moudni. "An autonomous vehicle sequencing problem at intersections: A genetic algorithm approach." International Journal of Applied Mathematics and Computer Science 23.1 (2013): 183-200. <http://eudml.org/doc/251322>.

@article{FeiYan2013,
abstract = {This paper addresses a vehicle sequencing problem for adjacent intersections under the framework of Autonomous Intersection Management (AIM). In the context of AIM, autonomous vehicles are considered to be independent individuals and the traffic control aims at deciding on an efficient vehicle passing sequence. Since there are considerable vehicle passing combinations, how to find an efficient vehicle passing sequence in a short time becomes a big challenge, especially for more than one intersection. In this paper, we present a technique for combining certain vehicles into some basic groups with reference to some properties discussed in our earlier works. A genetic algorithm based on these basic groups is designed to find an optimal or a near-optimal vehicle passing sequence for each intersection. Computational experiments verify that the proposed genetic algorithms can response quickly for several intersections. Simulations with continuous vehicles are carried out with application of the proposed algorithm or existing traffic control methods. The results show that the traffic condition can be significantly improved by our algorithm.},
author = {Fei Yan, Mahjoub Dridi, Abdellah El Moudni},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {autonomous vehicles; autonomous intersection management; genetic algorithm; dynamic programming; heuristics},
language = {eng},
number = {1},
pages = {183-200},
title = {An autonomous vehicle sequencing problem at intersections: A genetic algorithm approach},
url = {http://eudml.org/doc/251322},
volume = {23},
year = {2013},
}

TY - JOUR
AU - Fei Yan
AU - Mahjoub Dridi
AU - Abdellah El Moudni
TI - An autonomous vehicle sequencing problem at intersections: A genetic algorithm approach
JO - International Journal of Applied Mathematics and Computer Science
PY - 2013
VL - 23
IS - 1
SP - 183
EP - 200
AB - This paper addresses a vehicle sequencing problem for adjacent intersections under the framework of Autonomous Intersection Management (AIM). In the context of AIM, autonomous vehicles are considered to be independent individuals and the traffic control aims at deciding on an efficient vehicle passing sequence. Since there are considerable vehicle passing combinations, how to find an efficient vehicle passing sequence in a short time becomes a big challenge, especially for more than one intersection. In this paper, we present a technique for combining certain vehicles into some basic groups with reference to some properties discussed in our earlier works. A genetic algorithm based on these basic groups is designed to find an optimal or a near-optimal vehicle passing sequence for each intersection. Computational experiments verify that the proposed genetic algorithms can response quickly for several intersections. Simulations with continuous vehicles are carried out with application of the proposed algorithm or existing traffic control methods. The results show that the traffic condition can be significantly improved by our algorithm.
LA - eng
KW - autonomous vehicles; autonomous intersection management; genetic algorithm; dynamic programming; heuristics
UR - http://eudml.org/doc/251322
ER -

References

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