## 6 Conclusions and Future Challenges
### Future Challenges  
Several future challenges can be envisaged from this survey. These are summarised in the following lines:* Friction fusion (integration of slip-based and vibration-based approaches): It seems clear that a combination of different approaches will be needed in order to have a continuous estimation of the friction potential in spite of the driving situations [16; 104]. The precise definition of the driving states and the timely identification of these will be key aspects for the correct implementation of friction-fusion strategies.
* Robustness of type SAT-based approaches: Despite SAT-based approaches seem most promising among the slip-based group, there are several aspects to consider for future investigations. In particular, a more detailed evaluation during coupled dynamics (lateral and longitudinal forces) is still missing from the literature.
* Robustness of noise-based approaches: At the moment, high-frequency vibration-based approaches have been tested in a reduced number of scenarios. Additional investigations are still needed in order to find clear patterns that could facilitate the extraction of metrics to generalise the problem to a wider range of roads, tyres, and vehicles.
* Integration of ADAS with grip estimation approaches: Finally, the ultimate goal of the investigation on grip recognition approaches is to provide autonomous vehicles with the ability to detect low mu situations in a wide range of scenarios. Integrating the grip monitoring solutions on the ADAS and evaluating the impact of the uncertainty associated with the friction potential estimates on the ADAS performance will be of extreme importance in the future.  
This project is part of the Interdisciplinary Training Network in Multi-Actuated Ground Vehicles (ITEAM) European program and has received funding from the European Union's Horizon 2020 research and innovation program under the [PERSON] grant agreement No. 675999.  
The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} TRFC & Tyre Road Friction Coefficient \\ ABS & Anti-lock Braking System \\ TCS & Traction Control System \\ ESP & Electronic Stability Program \\ ADAS & Advanced Driver Assistance System \\ ACC & Autonomous Cruise Control \\ ADC & Autonomous Drift Control \\ SAT & Self-Aligning Torque \\ AFS & Active Front Steering \\ ANFIS & Adaptive Neuro-Fuzzy Inference System \\ CAN & Controller Area Networks \\ NN & Neural Networks \\ MF & Magic Formula \\ RLS & Recursive Least Squares \\ NLLS & Non Linear Least Squares \\ SMC & Sliding Mode Control \\ LRLS & Linearised Recursive Least Squares \\ PID & Proportional Integral Derivative \\ UKF & Unscented Kalman Filter \\ EKF & Extended Kalman Filter \\ MSE & Mean Squared Error \\ VFF & Vehicle Feature Fusion \\ DGPS & Differential Global Positioning System \\ EKBF & Extended Kalman Bucy Filter \\ GRNN & General Regression Neural Network \\ \end{tabular}