Analysis of Driving Control Model of Normal Lane Change Based on Naturalistic Driving Data

Jiarui Zhang1, Zhixiong Ma1, Xichan Zhu1, Yeting Lin1

  • 1Tongji University

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Category

Special Session

Sessions

11:00 - 12:00 | Mon 28 Oct | Crystal Room I | MoC-T5

Special Session on Big Data and Emerging Technologies for Traffic Safety Improvement (I)

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Abstract

The normal lane change is an optimal control process that continuously correct and adjust the steering. According to the theory of two-point visual control model, the normal lane change ideal trajectory was established based on optimum control principle of the steering wheel speed and the normal lane change characteristic parameters of naturalistic driving data. The verification results show that the ideal trajectory is generally consistent with the human lane-change path. The two-point visual control model is established with steering wheel speed and angle, the parameters of which are obtained and verified by using the natural driving normal lane change data, and the verification result is good. For different styles of drivers, the outcome of steering speed compliant with real human behaviors can be adjusted by a modification on the gain parameters of the model. The automated vehicles, which are developed based on this model, can fit the habits of human drivers and meet their comfort needs.

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