Modelling of a human driverʼ s interaction with vehicle automated steering using cooperative game theory

The introduction of automated driving systems raised questions about how the human driver interacts with the automated system. Non-cooperative game theory is increasingly used for modelling and understanding such interaction, while its counterpart, cooperative game theory is rarely discussed for similar applications despite it may be potentially more suitable. This paper describes the modelling of a human driverʼ s steering interaction with an automated steering system using cooperative game theory. The distributed Model Predictive Control approach is adopted to derive the driverʼ s and the automated steering systemʼ s strategies in a Pareto equilibrium sense, namely their cooperative Pareto steering strategies. Two separate numerical studies are carried out to study the influence of strategy parameters, and the influence of strategy types on the driverʼ s and the automated systemʼ s steering performance. It is found that when a driver interacts with an automated steering system using a cooperative Pareto steering strategy, the driver can improve his/ her performance in following a target path through increasing his/ her effort in pursuing his/ her own interest under the driver-automation cooperative control goal. It is also found that a driverʼ s adoption of cooperative Pareto steering strategy leads to a reinforcement in the driverʼ s steering angle control, compared to the driverʼ s adoption of non-cooperative Nash strategy. This in turn enables the vehicle to return from a lane-change maneuver to straight-line driving swifter.