## 1 Introduction
[19] utilized engine torque local convex minimum characteristic to estimate the vehicle mass. The major problem in using these approaches is based on the fact that the vehicle is required to be driven to obtain the proper persistent excitation and it usually takes a considerable time to estimate the real mass value. [PERSON] et al. [20] used information from suspension deflection sensors and a dual Kalman filter to estimate vehicle mass under static and dynamic conditions. Nowadays, vehicles equipped with active suspension incorporate these kinds of sensors. Suspension deflection can be directly measured through a potentiometer or an LVDT (linear variable differential transformer) which are both relatively economical and practical to be used. Taking into consideration all of the previous ideas and the fact that the studies of integrated brake force estimation system collaborated with road friction and vehicle mass estimators has not been sufficiently conducted, this paper proposes a novel longitudinal brake force estimation strategy for the individual wheel. Four major contributions clearly distinguish our endeavor from other studies:  
Unlike [14; 15; 16; 17; 18; 19], based on [20] but in a simpler way, the vehicle mass is instantly estimated by monitoring the static suspension deflections before the vehicle is driven by a driver or at a low speed of less than 10 km/h. Road friction identification uses an artificial neural network and average data set from currently available standard sensors. Therefore, compared to the model-based and slip-slop approaches [7; 8; 9; 10; 11; 12; 13], the accuracy of estimation can be fortified because ANN directly learns the road condition from the actual data. Using the average data for ANN is also a distinguishable idea to obtain smooth estimation outcomes. In addition, due to the off-line training of ANN, the proposed classifier is computationally light thus it is possibly applicable to ECU for the actual road test and is engaged for individual wheel thus the identification of road conditions for each wheel is doable. A combination of estimated mass, estimated road friction, and data-driven techniques is utilized to identify brake forces. The important aspect of this proposed brake force estimation structure relies on the estimation of the tire forces acting on each tire, without any reference to mathematical tire models [1; 2; 3]. Hence, compared to [4; 5; 6], it can be processed at a low computation cost, and a fast response, which makes the proposed method usable in middle-class cars with low-resolution sensors and low-performance microcontrollers. This work provides us with another possible solution for the integrated estimation approach of road friction and brake force including mass estimation. As mentioned earlier, it is true that studies on such integrated estimation systems are insufficient. Finally, the performance of the entire proposed estimation strategy is verified through several co-simulations between MATLAB/Simulink and a high-fidelity vehicle model from CarSim [21] according to different scenarios of velocities, road frictions, and masses. The rest of this paper is organized as follows: Section 2 presents the problem formulation. Section 3 introduces longitudinal slip ratio and vertical load of each tire used in this paper. The main works, mass estimation, road friction estimation, and individual brake force estimation, are proposed in Sections 4-6, respectively. Details about the integrated estimation system are shown in Section 7. Simulation results are presented in Section 8, and Section 9 concludes this paper.