# On-Board Road Friction Estimation Technique for Autonomous Driving Vehicle-Following Maneuvers
## 4 In-Vehicle Road-Grip Estimation  
### From Vehicle Sensors to Tires' State  
In recent years the number of sensors installed on vehicles has increased exponentially, facilitating the modelization of the entire system towards the target to consider the standard-instrumented vehicle as a mobile laboratory. Indeed, starting from the acquisition of the physical signals coming from all the sensors installed, employing currently widely-available and affordable mobile calculators, it is possible to properly process the time-evolving dynamic quantities with the aim to feed the real-time state estimators directly on-board. Furthermore, starting from the global quantities referring to the vehicle total behavior, it is currently possible to evaluate even the kinematic and dynamic states of its sub-components, as tires. The developed algorithm, based on the T.R.I.C.K. methodology described in [23], allows us to evaluate in a specifically dedicated on-board module the fundamental kinematic and dynamic quantities for the tire characterization in real time, starting from the experimental signals available within the vehicle CAN bus (Controller Area Network) and s-motion measurement or, as the case in exam, employing a specific set of sensors pre-configured on the vehicle. Such methodology also allows us to evaluate the potential of an estimation process in terms of tire interaction curves, such as in [48]. The originally designed model, described in [23], referred to a quadricycle vehicle fully described from the dynamic point of view. Since the study under analysis aims at simulating the emergency braking manoeuvres et similia, involving only the vehicle longitudinal dynamics, the model can be simplified considering its plane of symmetry **xz** (ISO reference system). Taking into account the above hypothesis, the vehicle can be represented as a bicycle model, whose constitutive equations are described by 3 degrees of freedom within the reference plane **xy**. The above assumption allows us to reduce also the analytical computational cost linked to the model state evaluation per step, as well as the number of parameters to be identified in order to physically reproduce the model dynamics concerning the longitudinal maneuvers, object of investigation. The simplified vehicle  
Figure 1: On-board Control Architecture. model, able to evaluate the kinematics and the dynamics at each axle, feeds the specifically designed logic of the control system providing both actual and potential friction coefficient in run-time. To perform the analyses, the following modelling and environment assumptions have been considered:  
* The road is modelled completely flat with eventual banking and local geometrical effects (i.e., potholes, kerbs, micro- and macro- roughness) absent. * The tire is modelled only in terms of its kinematic-dynamic transfer function without taking into account its eventual transient dynamics. Furthermore, the multi-physical effects, as thermal or wear abrasive and degradation influences, have not been taken into account at the current stage. * Since the vehicle is involved in analyses concerning only the longitudinal dynamic maneuvers and considering the vehicle body symmetry hypotheses, the steering angle signal is assumed to be always zero and, therefore, it is not employed within the modelling and the estimation of the vehicle state. * The vehicle is described only in terms of its intrinsic global geometric and mass-inertia parameters. The longitudinal load transfer is considered taking into account the position of the vehicle body centre of gravity. * The vertical load distribution on each axle is evaluated starting from the static load data, load transfers due to the geometric position of the vehicle body centre of gravity within the **xz** plane and the aerodynamic force.