4.4 Article

Nonlinear Driver Parameter Estimation and Driver Steering Behavior Analysis for ADAS Using Field Test Data

期刊

IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS
卷 47, 期 5, 页码 686-699

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/THMS.2017.2727547

关键词

Extended Kalman filter (EKF); field test; parameter estimation; two-point visual driver model; unscented Kalman filter (UKF); wavelet signal analysis

资金

  1. National Science Foundation [CMMI-1234286, CPS-1544814]
  2. Ford Motor Company

向作者/读者索取更多资源

In the development of advanced driver-assist systems (ADAS) for lane-keeping or cornering, one important design objective is to appropriately share the steering control with the driver. The steering behavior of the driver must therefore be well characterized for the design of a high-performance ADAS controller. 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 and validate this model in real-world scenarios. An extended Kalman filter and an unscented Kalman filter are implemented for estimating the driver parameters using either a joint-state estimation algorithm or a dual estimation algorithm. The estimated parameters for different types of drivers are analyzed and compared. The results show that the two-point visual driver model captures realistic driving behavior with time-varying, but not necessarily constant, parameters. A wavelet analysis of the driver steering command shows that distinct driver classes can be identified by analyzing the smoothness of the driver command using the Lipschitz exponents of the recorded signals.

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