Multi Agent System Optimization in Virtual Vehicle Testbeds

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.