# On-Board Road Friction Estimation Technique for Autonomous Driving Vehicle-Following Maneuvers
## 1 Introduction  
Rapid economic growth has lead to a considerable expansion of circulating vehicles, especially in big cities [1], exceeding the growth rate of the road infrastructure, and therefore leading to the traffic congestion [2], the growth of risk of accidents and fatalities [3, 4], as well as of pollution-linked issues, due to CO\({}_{\times}\), NO\({}_{x}\) and C\({}_{x}\)H\({}_{y}\) emissions [5, 6]. In recent years, thanks to the availability of continuously improving embedded hardware solutions, there has been a vast increase in the employment of advanced electronic systems to manage both safety and performance driving. Advanced Driver Assistance Systems (ADAS) are currently considered as a valued solution for reducing the main road transport issues (i.e., the congestion, traffic accidents, environmental stress and fuel consumption mentioned above) supporting the driver by informing on, actively assisting in, or taking over part of the driving task [7, 8, 9]. In a broader perceptive, by the superposition of sensing, planning, ADAS and control applications, the vehicle is going to become more and more automated in the very next future, thus leading to its full autonomy making the driver simply a passenger [10, 11]. Within this technological paradigm, the ability of the vehicles to drive themselves in a safe manner highly depends on their prior capability to understand the external environment and to correctly estimate the vehicle state in all the possible operating and environment conditions [12, 13, 14]. It is worth to note that, as stated by SAE International, the difference between a Level 4 and Level 5 autonomous vehicle is the capability of driving itself in anysituation, which implies adverse environmental scenarios like heavy rain, snow, or ice on the road surface [15]. Hence, in order to guarantee a greater safety-level with respect to environmental conditions [3; 4; 16; 17], it is necessary to account for their effect since from the very beginning of the ADAS design phase, introducing advanced control strategies that could leverage both real-time measurements, coming from different in-vehicles sensors (camera, radar, lidar and combinations of those via sensor-based fusion techniques [18; 19; 20; 21]), and on-board environmental estimation modules. Indeed, the use of only sensors' measurements could be not enough to perceive properly the external environment, since the vehicle control system has also to predict and discern how heavy rain, snow, ice condition or road singularities (e.g., oil stains, puddles, holes, or disconnected cobblestone) could impact on safety, so that the driving policy is to be tuned according to the actual environmental adversities. Moreover, in extreme scenarios vehicle dynamics may be deeply affected by the non-linearity of tires' dynamic behavior, therefore limiting the maneuverability in terms of both longitudinal and lateral accelerations and significantly reducing drive-ability and steer-ability. Furthermore, during emergency situations, which typically involve abrupt deceleration or steering, the tires can be easily pushed to their unstable dynamic region, thus requiring a specific control policy depending from the current dynamics of the vehicle and its sub-components, that hence have to be estimated at each time instant [22]. To solve the above open issues, in this paper, the authors propose a control architecture responsible for the longitudinal dynamics of the vehicle chassis, composed of two ADAS functionalities--namely Adaptive Cruice Control (ACC) and Autonomous Emergency Braking (AEB)--in addition to the blueAntilock Braking System (ABS), which is road-grip aware in the sense that it is able to properly regulate the vehicle motion on the base of the on-line estimation of the road friction coefficient per single tire, based on the T.R.I.C.K. (Tire Road Interaction Characterization & Knowledge) methodology [23].