Use of Naturalistic Driving Studies for Identification of Vehicle Dynamics

This paper discusses the feasibility of data captured in a long-term Naturalistic DrivingStudy (NDS) for identification of vehicle dynamics. Driving data were captured for over a year. In thisdata capture, there was minimal effort to define or control everyday driving practices. While the use ofreal-world data for model parameter identification is a well-known method, NDS are commonly used toexplore the behavior of drivers or to analyze real-world traffic situations. Data from NDS have not yetbeen used for the purpose of parameterizing vehicle dynamics models since everyday drives commonly donot reflect the full range of vehicle dynamics. This leads to the question if the data from an NDS containsthe needed information to describe vehicle dynamics accurately. This paper shows that data capturedfrom long-term everyday vehicle usage is sufficient to characterize vehicle dynamics models. It useslateral vehicle dynamics as an example to show how the data quantity changes the model accuracy androbustness. There is a point where any further data capture produces redundancy and does not add tothe overall information. The well-known single-track model serves as the modeling example which offersoptions to simply compare the derived model behavior with a reference.


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