Research on collision avoidance control of intelligent vehicles based on MSTUKF road adhesion coefficient identification

For the improvement of active safety and the reduction of traffic collisions, significant efforts have been made on collision avoidance technology. However, the majority of the current research focus on the relative motion of the vehicle, with less consideration of the road adhesion condition. This paper designs Multiple fading factors Strong Tracking Unscented Kalman Filter (MSTUKF) algorithm to identify road adhesion coefficient and proposes the collision avoidance strategy based on road adhesion. First, according to sensor information, the most dangerous target is selected. Then, multiple fading factors are introduced to construct MSTUKF algorithm, and combined with the vehicle dynamics model to identify the current road adhesion coefficient. For better collision avoidance effect, the dual risk assessment of vehicle and road are proposed, in which Time-To-Collision (TTC) is established according to the relative motion state of ego vehicle and the target, and Time-To-Avoidance (TTA) is calculated based on the road adhesion coefficient. Finally, using the feedforward and feedback control, the desired braking deceleration can be obtained by adjusting the master cylinder brake pressure. The simulation results show that the proposed algorithm can identify road adhesion coefficients with high accuracy and stability. Furthermore, the proposed strategy can provide good collision avoidance control effect under different scenarios.