Nonlinear Driver Parameter Estimation and Driver Steering Behavior Analysis for ADAS Using Field Test Data
| Autor: | Changxi You, Panagiotis Tsiotras, Jianbo Lu |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
050210 logistics & transportation
0209 industrial biotechnology Computer Networks and Communications Estimation theory Computer science Controller (computing) 05 social sciences Intelligent driver model Human Factors and Ergonomics 02 engineering and technology Kalman filter Computer Science Applications Human-Computer Interaction Extended Kalman filter 020901 industrial engineering & automation Design objective Artificial Intelligence Control and Systems Engineering Control theory 0502 economics and business Signal Processing Hidden Markov model Simulation Test data |
| Zdroj: | IEEE Transactions on Human-Machine Systems. 47:686-699 |
| ISSN: | 2168-2305 2168-2291 |
| DOI: | 10.1109/thms.2017.2727547 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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