The contact friction characteristic between a tyre and the road is a key factor that dominates the dynamics performance of a vehicle under critical conditions. Vehicle dynamics control systems, such as anti-lock braking systems, traction control systems, and electronic stability control systems (e.g. Elektronisches Stabilitäts Programm (ESP)), need an accurate road friction coefficient to adjust the control mode. No time delay in the estimation of road friction should be allowed, thereby avoiding the disappearance of the optimal control point. A comprehensive method to predict the road friction is suggested on the basis of the sensor fusion method, which is suitable for variations in the vehicle dynamics characteristics and the control modes. The multi-sensor signal fusion method is used to predict the road friction coefficient for a steering manoeuvre without braking; if active braking is involved, simplified models of the braking pressure and tyre force are adopted to predict the road friction coefficient and, when high-intensity braking is being considered, the neural network based on the optimal distribution method of the decay power is applied to predict the road friction coefficient. The method is validated through a ground test under complicated manoeuvre conditions. It was verified that the comprehensive method predicts the road friction coefficient promptly and accurately.
A new comprehensive driver model is presented for critical maneuvering conditions with more accurate dynamic control performance. In order to achieve a safe maneuvering mode, a new path planning scheme to maintain stability of the vehicle was designed. A new steering strategy, considering the errors of vehicle position and yaw angle between the real track and the planned path, was established to obtain the steering angle. Therefore, the vehicle can be adjusted to accurately follow the desired path with the driver model, and the stability of the vehicle and the smoothness of the steering angle input were comprehensively considered. Simulation results were used to validate the control performance in comparison with the optimal preview driver model proposed by Macadam.
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