Shared Control Driver Assistance System Based on Driving Intention and Situation Assessment

This paper presents a shared control driver assistance system based on the driving intention identification and situation assessment to avoid obstacles. A constrained linear-time-varying model predictive controller is designed to follow the obstacle-avoidance path, which is obtained by the artificial potential method in real time. A human driver's driving intention and the desired maneuver are recognized by the inductive multilabel classification with an unlabeled data approach that is trained based on the lateral offset and lateral velocity to the road center line. In addition, the situation assessment of the collision risk is represented by the time to collision and the performance evaluation is designed according to lateral deviation. All of them are employed for the design of the shared control fuzzy controller. The cooperative coefficient, denoting the control authority between the controller and a human driver, is determined by three fuzzy controllers in different conditions, which are the consistent, the advanced inconsistent, and the lagged inconsistent fuzzy controller, respectively. More importantly, there are two scenarios studies provided to verify the proposed system. The results prove that the shared control driver assistance system can successfully help drivers to avoid obstacles and obtains great vehicle stability performance in different scenarios.