Application of Game Refinement Theory to Automated Driving

Abstract

In recent years, assisted driving and self-driving have captured the imagination of manufacturers, designers, technology providers, and the general public with the expectation of a sustainable, safer, and intelligent mobility in the near future. Self-driving or assisted driving vehicles are complex systems that integrate environmental perception, intelligent planning and decision-making, tracking, and control. With the increasing intelligence of vehicles, personalized design is an inevitable trend. A design that is in line with the driver’s personality can bring a better driving experience to the driver. Thus, classifying driving types while driving in a self-driving environment may play an important role in the construction of trajectory planning algorithms. This paper uses the motion-in-mind model from game refinement theory to model driver behavior. Further, a classification of the model parameters into three categories helped in distinguishing cautious, aggressive, and average drivers. The results showed that the self-driving environment can be successfully modeled as a game and adaptation to match the riders’ driving skills may improve satisfaction.