# On-Board Road Friction Estimation Technique for Autonomous Driving Vehicle-Following Maneuvers
## 8 Conclusions  
This paper is focused on the development of a new control architecture for vehicles, based on the estimation of the maximum achievable road friction coefficient.  
The proposed technique for real-time road friction estimation in different environmental conditions is based on both bicycle model to evaluate the state of the vehicle and a tire Magic Formula model based on a slip-slope approach. The introduction of the grip value in ADAS application, in particular for the longitudinal dynamics of the vehicle chassis, composed of Adaptive Cruise Control (ACC) and Autonomous Emergency Brake (AEB), and the Antilock Braking System (ABS), allows the vehicle to work at the maximum performance in all operating conditions.  
In this study, the technique has been performed on a simulation platform, consisting of different components working simultaneously. A vehicle dynamics model, implemented in the MAT-LAB/Simulink environment, has been employed for the evaluation of the control logic performance in vehicle-following maneuvers. In addition, a SUMO environment has been implemented to realize different traffic scenarios and environmental conditions. Finally, the Friction algorithm has been used to perform the estimation of the road coefficient.  
The simulation has been carried out in different environmental and vehicle kinematic conditions. According to the results, the control system has been improved by the involvement of the current and potential friction coefficient evaluated in run-time. The improvement basically results from the development of a control system that is able to avoid collisions in any environmental condition. The potential grip has been therefore demonstrated to be crucial for the autonomous driving systems.  
Future developments will also comprehend the lateral dynamics. Several authors have already tackled the problem describing different techniques and modelling approaches [63; 64; 65]. The authors plan also to include the impact of the road bank angle and slope, the tire combined interaction characteristics, as well as, the variations of the vehicle dynamic behaviour due to the tire intrinsic multi-physics (i.e., wear and temperature effects) [66; 67].  
Conceptualization and supervision, S.S. and A.S.; methodology, S. S., V.M.A. and A.S.; software and investigation, N.A. and V.M.A.; formal analysis, S.S.; resources and visualization, R.B.; data curation, N.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scenario** & **min TTC** & **min \(\mathbf{\lambda}\)** & **min d(t)** \\ \hline Vehicle following with estimation & 2.02 & \(-\)0.15 & 10.30 \\ Vehicle following w/o estimation & 0 & \(-\)1.08 & 0 \\ Stop\&Go with estimation & 2.73 & 1.22 & 3.20 \\ Stop\&Go w/o estimation & 2.13 & 0.48 & 2.70 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of the minimal values assumed by the safety indexes in every described scenario.