# Combined Identification of Vehicle Parameters and Road Surface Roughness Using Vehicle Responses
###### Abstract
[15] measured the acceleration responses of a four-degree-of-freedom semi-vehicle with known parameters and used the Newmark method to solve the wheel contact forces and identify the road roughness from the wheel contact forces. [PERSON] et al. [16] took tire and body acceleration as an input of the Long Short-Term Memory (LSTM) network to classify roughness which is extended to the state vector and reconstructed by the AKF. [PERSON] et al. [17] proposed an augmented Kalman filter algorithm considering the longitudinal acceleration term of vehicles to identify road roughness, but this method requires calibrated vehicles with known parameters, which limits its wide application in practice. The dynamic interaction between vehicles and roads is a complex dynamic process [18; 19]. In the indirect estimation method of roughness based on vehicle response, the dynamic characteristics of the vehicle model play the important role. When vehicle parameters are not clear, it is more difficult to accurately identify the road roughness. Many scholars have proposed the staged identification method for vehicle parameters and road roughness. [PERSON] et al. [20] established a transfer function using bump and measured vehicle suspension deflection to identify vehicle parameters in the state space and estimate road roughness profiles based on a discrete Kalman filter under unknown excitation (DKF-UI). [PERSON] et al. [21] used a smartphone to measure vehicle responses as it passed bumps, optimized unknown vehicle parameters and speed using genetic algorithm (GA) based on the minimized error between measured and predicted vehicle responses, and estimated the road roughness by combining Rauch-Tung-Striebel (RTS) smoothing and AKF. [PERSON] et al. [22] used a laser profiler to measure the roughness of the test road section as the observation measure of the recursive least squares estimation (RISE) to calibrate vehicle parameters which were used to identify the road roughness of an unknown road based on a Kalman filter under unknown excitation (DKF-UI). [PERSON] et al. [23] took the error between the power spectral density (PSD) of vehicle accelerations calculated by the frequency response function and the PSD of measured vehicle accelerations under bump excitation as the objective function, and optimized the vehicle parameters through GA. Based on the derived conversion relationship between tire pressure and contact force, the contact force is taken as the unique observation of DKF-UI to identify the road roughness. [PERSON] et al. [24] measured the free attenuation responses of a vehicle passing through a speed bump, from which the natural frequency and damping ratio were obtained, and iteratively identified the relevant physical parameters through the sensitivity analysis of the frequency. Then, the expression of the vehicle frequency response function relative to the wheel contact roughness was derived from the vehicle motion equation in the frequency domain, and road roughness was estimated based on the measured vehicle response and the frequency response function. Some scholars have also proposed the method of directly identifying the road roughness without identifying the vehicle parameters. [PERSON] et al. [25] used the bump of known size to establish the frequency response function of the vehicle response and the displacement of the contact point, and estimated the road roughness based on the frequency response function and the measured vehicle responses. However, all the above identification methods required additional test settings, such as the test of the vehicle passing through a speed bump, vehicle vibration tests [26], or test sections with known roughness; frequent large-scale road network monitoring requires simple model calibration methods. Vehicle parameters significantly affect the dynamic characteristics of the vehicle and the road surface. The coupling of unknown parameters and unknown roughness makes it difficult to simultaneously identify the vehicle parameters and the road roughness.