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Vehicle speeds, in both longitudinal and lateral directions, are vital signals for vehicular electronic control systems. In in-wheel motor-driven vehicles (IMDVs), because no slave wheel can be used for reference, it becomes more challenging to conduct velocity estimation, especially when all wheels turn to slip. To reduce the dependence of speed estimation on physical plant parameters and environment perception, in this work, we develop a new method that estimates the longitudinal and lateral velocities of an IMDV by using the kinematic model with the Kalman Filter. For longitudinal velocity measurement, we propose a hybrid approach combining Long-Short Term Memory (LSTM) networks and the kinematic rules to obtain a reliable estimation. More specifically, when at least one effective driven wheel is available, that is, no-slip happening, the longitudinal velocity can be derived using the average of those effective wheels' rotational speeds.

Accurate information about tire cornering stiffness is essential for the implementation of advanced vehicular control systems. Data-driven modelling method leverages the availability of high-quality measurement data alone, without vehicle parameters, which provides a tutorial to reconstruct the system dynamics and estimate tire cornering stiffness. As such, we collect the states and inputs of the vehicle to build its state space using the dynamic mode decomposition (DMD) method. Then, based on the entries of the system and input matrix, the tire cornering stiffness can be further identified by solving the linear equations via orthogonal regression with considering the measurement noise. The sufficient and necessary rank condition for the DMD execution is also analyzed. Additionally, we introduce two alternative ways to update the system and input matrices - recursive least squares (RLS) and sliding window (SW).