Driver Parameter Estimation Using Joint E-/UKF and Dual E-/UKF Under Nonlinear State Inequality Constraints

In the development of advanced driver-assist systems (ADAS) for lane-keeping, one important design objective is to appropriately share the steering control with the driver. Hence, the steering behavior of the driver must be well known beforehand. This paper adopts the well-known two-point visual driver model to characterize the steering behavior of the driver, and conducts a series of field tests to identify the model parameters to validate the two-point visual driver model in real scenarios. Both an extended Kalman filter and an unscented Kalman filter are implemented for estimating the unknown driver parameters, using a joint-state estimation algorithm and a dual estimation algorithm, and the results are compared.