This paper proposes a cooperative game-theoretic framework for driver-automation interaction control. It features personalized assistance through online identification of driver steering characteristics. A two-point preview-based distributed model predictive control (MPC) strategy is developed to model the interactive behaviour between the human driver and the active front steering (AFS) system of a passenger car. The proposed strategy leverages cooperative game theory, formulating a joint optimisation of the steering actions of both the driver and the AFS based on Pareto equilibrium. To account for inter-driver variability in steering behaviour and deliver tailored assistance, a recursive least squares (RLS) algorithm with exponential forgetting is applied to identify driver steering characteristics online. The estimated driver steering characteristics are then used to compute the AFS steering angle that supports the driver's steering actions. Two numerical studies are carried out. The first study demonstrates the effectiveness of the Pareto-equilibrium steering strategies, incorporating the two-point-preview setup in achieving satisfactory vehicle path-tracking performance. The second study validates the proposed online identification scheme under different driver-imposed path-tracking penalties as well as different weight allocations between the driver and the AFS controller.