The vehicle state information plays an important role in the vehicle active safety systems; this paper proposed a new concept to estimate the instantaneous vehicle speed, yaw rate, tire forces, and tire kinemics information in real time. The estimator is based on the 3DoF vehicle model combined with the piecewise linear tire model. The estimator is realized using the unscented Kalman filter (UKF), since it is based on the unscented transfer technique and considers high order terms during the measurement and update stage. The numerical simulations are carried out to further investigate the performance of the estimator under high friction and low friction road conditions in the MATLAB/Simulink combined with the Carsim environment. The simulation results are compared with the numerical results from Carsim software, which indicate that UKF can estimate the vehicle state information accurately and in real time; the proposed estimation will provide the necessary and reliable state information to the vehicle controller in the future. 1. Introduction Active safety is all about technical solutions that help the drivers avoid accidents or significantly reduce the possibilities of the accidents. These include braking systems, like ABS, traction control systems, and electronic stability control systems that interpret the signals from various sensors to help drivers control the vehicle or give a warning to driver under various road conditions as well as the driving maneuvers. And the performances of the controller rely heavily on the accurate vehicle states information. Some of the required vehicle state information is easy to measure by the sensors which exist in model vehicle, such as the spin speed of four wheels, but others are difficult to detect due to the expense, complexity, and the technological limits. Reducing sensor is a potential approach to cut down the cost and improve the reliability and performance of the controller. State estimation is an algorithm which prides the internal state of a given system in real time; the algorithm uses the online measurements as inputs and obtains the estimated values. It provides the basis of many practical applications and attracts increasing attention in the field of automobile industry, especially in the vehicle active safety research field [1–4]. Accurate and real-time vehicle state information is necessary for the vehicle dynamic controller; there are a lot of researchers who have proposed some approaches to estimate the vehicle state and parameters. Zhao et al. [5] proposed the tire force estimation method by
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