## 1 Introduction  
Tyre forces influence largely the chassis stability and manoeuvrability [1]. These forces are limited by the maximum friction that can be generated between the tyre carcass and the road surface, which is the result of complex phenomena on the rubber-road interface. According to [2], such phenomena comprise molecular adhesion and indentation between the road irregularities and the tyre rubber. Despite rigorous studies having been developed to understand such interactions, vehicle dynamics engineers often feel more comfortable reducing these analyses to the estimation of a normalised coefficient, often called road friction potential [3] or tyre-road friction coefficient (TRFC) [4; 5]. The estimation of the road friction potential has been studied exhaustively during recent years [3; 6; 7; 8; 9; 10; 11]. Specifically, it is envisaged that the accurate determination of such a coefficient can contribute to improve the performance of current vehicle systems such as Anti-Lock Braking System (ABS), Traction Control System (TCS), or Electronic Stability Program (ESP) [12; 13; 14]. An important drawback remarked by several authors is that a high excitation level (e.g., an emergency braking manoeuvre) is necessary in order to obtain an accurate estimation of the road grip potential when traditional slip-based approaches are employed [15; 16; 17; 18]. Nevertheless, as the previous systems are triggered when a significant excitation is present, the friction coefficient can be estimated during the system intervention [19; 20] and the system thresholds can be readjusted in parallel. With the development of modern Advanced Driver Assistance Systems (ADAS), new stricter requirements regarding the road friction estimation have arisen [21; 22; 23; 24]. Novel functions such as Adaptive Cruise Control (ACC) [25], Autonomous Emergency Braking (AEB) [26] or drift-based lateral collision avoidance [22; 27; 28] require an accurate and timely estimation of the maximum road grip prior to the intervention of the system. Thus, the road grip potential needs to be continuously monitored during free-rolling, coast-down or gentle acceleration events to correct critical variables such as the minimum separation between vehicles depending on the available friction. Moreover, novel drift-based applications not only require an estimation of the maximum road grip potential, but also a more detailed characterisation of the tyre-road friction (e.g., force versus slip curve [3; 27; 29; 30; 31]). Several approaches have been discussed in the literature to estimate the road grip potential. A first classification provided in [7] establishes a distinction between cause-based and effect-based approaches. While cause-based approaches focus on estimating the road grip potential from aspects such as the lubricant present at the road surface [32; 33], effect-based approaches infer the road grip from the tyre responses (e.g., slip [34; 35]). Despite the fact that the former approaches can provide an accurate estimation of the grip potential during free-rolling driving conditions, an important handicap of these is that a database and extensive training is required in order to achieve an accurate correlation between the monitored road property and the road grip potential [7]. Therefore, such approaches might present insufficient correlation [36] or extrapolation issues where situations not included in the training dataset are faced. Regarding effect-based approaches, tyre slip-based, tyre vibration-based and tyre noise-based works have been found in the literature. A general trend exhibited by slip-based approaches is that a significant excitation level (up to 80-90% on low-mu conditions [16]) is required to provide an accurate estimation of the road grip potential [37]. Such excitation thresholds have been reduced during recent years employing tyre-self alignment-based estimation methods [9; 38; 39; 40; 41]. These approaches take advantage of the higher sensitivity exhibited by the tyre self-alignment torque to changes in the road friction coefficient.