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Robust Shared Lateral Control for Autonomous Vehicles

The challenges existing under the category of fully autonomous systems call for a need of human automation interaction to ensure safety and trust. Motivated by the above, this paper deals with the design of a shared control framework that enables the interaction between the human driver and automation. Further, the potential of game theory in a cooperative framework is employed to model the strategic interaction between the human driver and automation. The lateral dynamics of the vehicle model is taken into consideration with an incomplete information of all states. Lateral displacement and Yaw angle are measured whereas lateral velocity and Yaw rate are the unavailable states. A higher Order sliding Mode (HOSM) observer is designed to estimate the unknown states. With the availability of the estimated states, the interaction between the human driver and automation is carried out to generate a shared control law based on cooperative game theory. Model predictive control (MPC) approach is employed to design the control action for the human driver and autonomous subsystem separately. Then, the proposed shared lateral control scheme is analyzed and examined through simulation to evaluate the driver performance in this cooperative game theoretic approach.