## 1 Introduction
Still, a certain level of grip utilisation is required, which according to the literature ranges between 30% and 50%. For pure longitudinal conditions, some authors argue that a clear correlation can be established between the road friction potential and the tyre longitudinal stiffness [6; 7; 42; 43; 44], and therefore the maximum road grip can be estimated if the previous tyre parameter is inferred from the vehicle longitudinal dynamics. On the other hand, recent experiments demonstrate that the longitudinal stiffness or slip slope does not change significantly when different high and low mu rigid surfaces are tested [45]. Therefore, the slip slope approaches might approximate well for severe changes in the road friction (e.g., from dry asphalt to gravel), but fail to estimate mu-jumps on rigid surfaces (e.g., colgrip board jump to painted board [45]). This discussion is continued in the following sections. The limitations exhibited by slip-based approaches (mainly the necessity of generating large slip values) have encouraged researchers to explore low frequency [46; 47; 48; 49] and high frequency [50; 4; 51; 5] vibration-based approaches. The main objective of these solutions is to achieve an accurate grip potential identification during regular driving conditions. The general trend of noise-based solutions lies in the acquisition and subsequent post-processing of the high-frequency noise emitted by the tyre-road interface. Then, a suitable feature vector is designed and a classifier is trained to estimate the grip potential when new samples are available. Classifiers to distinguish roads of different roughness [46] as well as wetness [49] are present in the literature. For low frequency vibration-based approaches, a wide range of solutions have been adopted, among which the analysis of the influence of the road grip potential on the frequency response of the driveline or steering system stands out in the literature. In particular, some authors have indicated that the tyre longitudinal stiffness can be correlated with the resonance frequency of the transfer function relating tyre torque disturbances and wheel speed [50; 4]. In addition, authors found an empirical correlation between the tyre longitudinal stiffness and the road grip potential following the same slip slope concept employed in preceding longitudinal slip-based approaches [7; 44]. Thus, if the previous correlations are employed, the road friction potential can be estimated from the wheel speed frequency responses. To sum up, a complete picture of the road friction estimation problem discussed in this review is provided in Figure 1. The analysis is limited to effect-based approaches as these have received greater interest during recent years due to their cost-effectiveness and robustness. For additional discussions on cause-based approaches [7] can be consulted. The rest of the paper is organised as follows. In Section 2 relevant background regarding vehicle modelling and tyre friction is provided. Moreover, the section is completed with a brief discussion regarding the potential impact of an accurate estimation of the road grip potential on current vehicle systems and envisaged future ADAS systems. The paper continues with a comprehensive discussion on slip-based road friction recognition approaches in Section 3. As remarked previously, special emphasis is put on the excitation levels required to achieve an accurate estimation of the grip potential. The same line of discussion is followed when tyre vibration-based (high and low frequency) are considered in Section 4. Finally, a comprehensive summary of the different approaches presented in this work is provided in Section 5 and conclusions and future research steps are detailed in Section 6.