Game Theoretic Modeling of Driver and Vehicle Interactions for Verification and Validation of Autonomous Vehicle Control Systems

• Published in: IEEE transactions on control systems technology Vol. 26; no. 5; pp. 1782 - 1797
• Main Authors: Nan Li, Oyler, Dave W., Mengxuan Zhang, Yildiz, Yildiray, Kolmanovsky, Ilya, Girard, Anouck R.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.09.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Automobiles; Autonomous cars; Autonomous vehicles; Calibration; Computational modeling; Computer simulation; Control algorithms; Control systems; Drivers; Game theory; Games; Hidden Markov models; Parameters; reinforcement learning (RL); Road tests; Simulators; traffic modeling; Traffic models; verification and validation (V&V)
• ISSN: 1063-6536; 1558-0865
• DOI: 10.1109/TCST.2017.2723574

Autonomous driving has been the subject of incre- ased 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 interactions among multiple drivers and vehicles occur simultaneously. Traffic simulators where these interactions can be modeled and represented with reasonable fidelity can help to 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.