A novel scheme on adaptive cruise strategy for intelligent vehicles considering pavement types

Establishing an adaptive cruise strategy which can have an outstanding performance under various pavement types is extremely necessary to decision making for intelligent vehicles. However, existing research for tyre-road friction coefficient (TRFC) estimation ignore the influence of state mutation and noise uncertainty, which will lead to unsatisfactory estimation precision. To address these problems, adaptive strong tracking square-root cubature Kalman filter (ASTSCKF) is proposed to guarantee the precision for TRFC estimation, which is composed of Sage-Husa noise estimator and strong tracing filtering. Simulation and vehicle testing based on the simulator platform indicate that ASTSCKF is capable of estimating TRFC more accurately than extended Kalman filter (EKF) and unscented Kalman filter (UKF), showing strong anti-interference to different pavement types. The proposed cruise scheme also displays an excellent tracking performance on the leading vehicle, which verifies the feasibility of the cruise scheme.