The contact between the tire and the road is the key enabler of vehicle acceleration, deceleration and steering. However, due to changes to the road conditions, the driver`s ability to maintain a stable vehicle maybe at risk. In many cases, this requires intervention from the chassis control systems onboard the vehicle. Although these systems perform well in a variety of situations, their performance can be improved if a real-time estimate of the tire-road contact parameters (ranging from kinematic conditions of the tire to its dynamic properties) is available. This paper presents the implementation strategy for a real-time tire-road contact parameter estimation methodology using accelerometric (tri-axial) signals from an intelligent tire. Through post-processing of the acquired acceleration signals, it is possible to determine in real-time, information about the dynamic vertical load exerted on the tire and the tire slipangle. An observer developed using the sliding mode approach is used to make an estimate of the lateral and longitudinal tire forces. This method strictly uses measurements from sensors potentially integrable or already integrated in current production vehicles (yaw rate, longitudinal /lateral accelerations and steering wheel angle). An Extended State Observer (ESO) has been developed to make an estimate of the vehicle's longitudinal velocity and lateral velocity and hence the slip-ratio (assuming wheel rotational speed information is available from the ABS unit). An artificial neural network (ANN) based data fusion approach, combining the intelligent-tire information with the observer information was used to make a real-time estimate of the tire-road friction coefficient. Considering the strong interdependence between the operating road surface condition and the instantaneous forces and moments generated; this real time estimate of the tire-road friction coefficient is expected to play a pivotal role in improving the performance of a number of vehicle control systems. In particular, this paper focuses on the possibility of enhancing the performance of the ABS control by using the additional information provided by the intelligent tire. In order to achieve the aforementioned objectives, the design and implementation of a Fuzzy/Sliding Mode/Proportional Integral (Fuzzy-SMC-PI (FSP)) control methodology is proposed. The performance of the proposed system was evaluated through simulations on a series of braking maneuvers, emphasizing on jump - mu conditions. The results show significant improvements in the stopping distance of a vehicle equipped with an intelligent tire based FSP controller as compared to a vehicle equipped with a standard ABS.