2016/08/30 by Nan Li, Dave W. Oyler, Li, Nan +11 · 6 citations
Computer Science · Engineering · #Artificial Intelligence (cs.AI) #Autonomous Vehicle Technology and Safety #Computer Science and Game Theory (cs.GT) #FOS: Computer and information sciences #Traffic control and management #Vehicle Dynamics and Control Systems #cs.AI #cs.GT
paper · pdf · doi:10.48550/arxiv.1608.08589
13 pages, 16 figures
arxiv created 2016/08/30 · openalex publication_date 2016/08/30 · arxiv updated 2016/08/31 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Autonomous driving has been the subject of increased interest in recent years both in industry and in academia. Serious efforts are being pursued to address legal, technical and logistical problems and make autonomous cars a viable option for everyday transportation. One significant challenge is the time and effort required for the verification and validation of the decision and control algorithms employed in these vehicles to ensure a safe and comfortable driving experience. Hundreds of thousands of miles of driving tests are required to achieve a well calibrated control system that is capable of operating an autonomous vehicle in an uncertain traffic environment where multiple interactions between vehicles and drivers simultaneously occur. Traffic simulators where these interactions can be modeled and represented with reasonable fidelity can help decrease the time and effort necessary for the development of the autonomous driving control algorithms by providing a venue where acceptable initial control calibrations can be achieved quickly and safely before actual road tests. In this paper, we present a game theoretic traffic model that can be used to 1) test and compare various autonomous vehicle decision and control systems and 2) calibrate the parameters of an existing control system. We demonstrate two example case studies, where, in the first case, we test and quantitatively compare two autonomous vehicle control systems in terms of their safety and performance, and, in the second case, we optimize the parameters of an autonomous vehicle control system, utilizing the proposed traffic model and simulation environment.