Model Predictive Control Based Multifunctional Advanced Driver-Assistance System Specialized for Rear-End Collision Avoidance

Abstract

This paper presents the model predictive control (MPC) based multifunctional advanced driver-assistance system (MADAS) that is optimized for rear-end collision avoidance. First, the system's operation is judged by considering the driver's intention of avoidance and the possibility of avoiding obstacle vehicles. Once the system is activated, the lateral tire force corresponding to the driver's steering input, which is essential for collision avoidance, is realized with the highest priority. The use of each tire friction circle is then maximized by utilizing available tire forces for braking through quadratic programming. While the MADAS ensures the lateral maneuver and deceleration of the vehicle, the system still can generate additional yaw moment calculated from the MPC, the upper level controller, to track the driver's desired yaw rate or prevent the vehicle from becoming unstable. The nonlinearity inevitably encountered in maximizing tire forces is reflected through the extended bicycle model and the combined brushed tire model. The proposed system is verified by the vehicle dynamics software CarSim, and the simulation results show that the MADAS performs better in rear-end collision avoidance situations than conventional advanced driver-assistance systems (ADAS).