Slippery road surface poses challenges to drivers and is a frequent contributor to serious or fatal car accidents.Today many advanced vehicle control systems are designed with the aim of helping drivers to avoid accidents.The rohustness and reliability of these systems can be greatly enhanced if the information of a road surface condition is available for use in vehicle control design.In this paper,a method is provided for online identification of road surface condition using model-based estimation techniques.The proposed approach does not require additional sensors or severe vehicle maneuvering.In the proposed approach,road surface conditions are firstly pre-categorized into non-slippery and slippery states.Two vehicle reference models are defined to represent vehicles operating under non-slippery and slippery road surfaces respectively.An index variable that reflects the vehicle understeer characteristics is extracted,with good sensitivity to the changes of road surface conditions.Indices are determined from the reference models under the non-slippery road surface,the slippery road surface,and from vehicle sensor measurements.The calculated indices are further processed using root mean square deviation for better differentiation and identification of the road surface condition.The proposed approach has been verified via both computer simulation and vehicle testing,with results showing its robustness and effectiveness in practical applications.
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