Comparing a multiobjective optimization algorithm for discovering driving strategies with humans

► When a person drives a vehicle along a route, he/she optimizes several objectives. ► The task of driving can be solved also with appropriate optimization algorithms. ► We designed a multiobjective algorithm for discovering driving strategies. ► The algorithm always finds better driving strategies...

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Bibliographic Details
Published in:Expert systems with applications 2013-06, Vol.40 (7), p.2687-2695
Main Authors: Dovgan, E., Javorski, M., Tušar, T., Gams, M., Filipič, B.
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Decision theory. Utility theory
Driving optimization
Driving strategy
Exact sciences and technology
Expert systems
Fuel consumption
Human
Human driving
Mathematical programming
Multiobjective optimization
Operational research and scientific management
Operational research. Management science
Optimization
Strategy
Tasks
Traveling time
Vehicles
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Summary:► When a person drives a vehicle along a route, he/she optimizes several objectives. ► The task of driving can be solved also with appropriate optimization algorithms. ► We designed a multiobjective algorithm for discovering driving strategies. ► The algorithm always finds better driving strategies than a group of volunteers. When a person drives a vehicle along a route, he/she optimizes two objectives, the traveling time and the fuel consumption. Therefore, the task of driving can be viewed as a multiobjective optimization problem and solved with appropriate optimization algorithms. The comparison between the driving strategies obtained by humans and those obtained by the algorithms is interesting from several points of view. For example, it is interesting to see which strategies are better. To perform the human versus machine test, we compared the driving strategies obtained by the multiobjective optimization algorithm for discovering driving strategies (MODS) with those obtained by a group of volunteers operating a vehicle simulator. The test was performed using data from three real-world routes. The results show that MODS always finds better driving strategies than the volunteers, especially when the fuel consumption is to be reduced. Moreover, the results show that some volunteers always drive similarly in terms of traveling time and fuel consumption while others significantly vary their driving strategies.
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2012.11.006