Vehicle lateral motion including lane change, merging, and turning is a major contributor to traffic accidents. Realistic modeling of drivers’ lateral motion control can not only improve understanding of the mechanisms that trigger these accidents but also permit the design of advanced systems that are capable of warning drivers of potential hazard during lateral movements. Departing from conventional, descriptive approach to modeling vehicle lateral motion which fits statistical models to field data, this research takes an explanatory approach by capturing the mechanism that underlies drivers’ lateral motion control. A clear understanding of what factors are involved in driver lateral control and how these factors function in driver decision-making will lead to the formulation of Lateral Control Model. This model incorporates lane changing and gap acceptance decisions into a single model and applies to both highways and intersections. This research responds to the Grant Theme of Safety. In particular the research focused on not only human factors research as it relates to elderly drivers but also technology-related research which leads to better traffic simulators and on-board lateral collision warning systems.
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In recent decades, great strides have been made to lower the number of accidents that occur on Texas roadways through graduated drivers licensing prog...
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United States Department of Transportation
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