On-board road condition monitoring system using slip-based tyre-road friction estimation and wheel speed signal analysis

Abstract

This paper presents an on-board road condition monitoring system. The road condition is continuously evaluated in terms of slipperiness and coarseness and is classified into four grades, normal (μ_max ≤ 0.5), slippery (0.3 ≤ μ_max< 0.5), very slippery (μ_max< 0.3), and rough surface (gravel). A non-linear curve fitting technique is adopted to estimate the maximum tyre-road friction coefficient using the so-called ‘magic formula’. The characteristic of the relationship between friction coefficient and slip, i.e. the value of maximum friction coefficient μ_max varies significantly with different surfaces, but its corresponding slip value λ_max does not vary much, is exploited in the road condition classification algorithm. For surface coarseness analysis, a separate classifier based on the variance of filtered wheel speed signal is implemented. Experimental results demonstrate the feasibility of the road condition monitoring system for detecting slippery and rough road surfaces in close to real-time. In addition, the proposed slip-based friction estimation algorithm has the merits of robustness to vehicle-tyre variance and easy calibration as opposed to past slip-based friction estimation approaches in the literature.