sc 16(2): e1

Research Article

Multi Agent System Optimization in Virtual Vehicle Testbeds

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  • @ARTICLE{10.4108/eai.24-8-2015.2261104,
        author={Patrick Lange and Rene Weller and Gabriel Zachmann},
        title={Multi Agent System Optimization in Virtual Vehicle Testbeds},
        journal={EAI Endorsed Transactions on Smart Cities},
        volume={1},
        number={2},
        publisher={ACM},
        journal_a={SC},
        year={2015},
        month={8},
        keywords={virtual testbed, vehicle simulation, multi agent system, discrete event simulation, gpu, cuda, domain specific modelling},
        doi={10.4108/eai.24-8-2015.2261104}
    }
    
  • Patrick Lange
    Rene Weller
    Gabriel Zachmann
    Year: 2015
    Multi Agent System Optimization in Virtual Vehicle Testbeds
    SC
    EAI
    DOI: 10.4108/eai.24-8-2015.2261104
Patrick Lange1,*, Rene Weller1, Gabriel Zachmann1
  • 1: University of Bremen
*Contact email: lange@cs.uni-bremen.de

Abstract

Modelling, simulation, and optimization play a crucial role in the development and testing of autonomous vehicles. The ability to compute, test, assess, and debug suitable configurations reduces the time and cost of vehicle development. Until now, engineers are forced to manually change vehicle configurations in virtual testbeds in order to react to inappropriate simulated vehicle performance. Such manual adjustments are very time consuming and are also often made ad-hoc, which decreases the overall quality of the vehicle engineering process. In order to avoid this manual adjustment as well as to improve the overall quality of these adjustments, we present a novel comprehensive approach to modelling, simulation, and optimization of such vehicles. Instead of manually adjusting vehicle configurations, engineers can specify simulation goals in a domain specific modelling language. The simulated vehicle performance is then mapped to these simulation goals and our multi-agent system computes for optimized vehicle configuration parameters in order to satisfy these goals. Consequently, our approach does not need any supervision and gives engineers visual feedback of their vehicle configuration expectations. Our evaluation shows that we are able to optimize vehicle configuration sets to meet simulation goals while maintaining real-time performance of the overall simulation.