Multiobjective discovery of human-like driving strategies

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Abstract
Human driving models aim at producing human-like driving strategies by mimicking the behavior of drivers. Drivers optimize several objectives when traveling along a route, such as the traveling time and the fuel consumption. However, these objectives are not taken into account when building human driving models. To overcome this shortcoming, we designed a two-level Multiobjective Optimization algorithm for discovering Human-like Driving Strategies (MOHDS) that combines the human driving models with the optimization of the traveling time and the fuel consumption. Consequently, MOHDS enables to simultaneously mimic human driving behavior and optimize relevant driving objectives. MOHDS was tested on a two-lane rural route and compared to the existing approaches for human driving modeling. The results show that, unlike the existing approaches, MOHDS finds the driving strategies with various tradeoffs between the objectives.
Funding Information
  • The Slovenian Research and Innovation Agency (Z2-7581)
  • Horizon 2020 Framework Programme (692286)
  • NERVteh, raziskave in razvoj, d.o.o.

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