This paper describes the design of a new haptic shared steering control framework for automated driving systems. In this framework, the shared control problem is formulated as a constrained optimization problem which is solved online by a model predictive controller. Without driver's intervention, the system assumes automatic lane-keeping control. When the driver takes over control, by adapting the weight on the stage cost and implementing dynamic constraints, the framework ensures seamless control transfer from the system to the driver while conveying potential hazards through haptic feedback. Simulation results are presented to demonstrate the ability of this framework to handle control allocation and hazard warning.