Adaptive Cruise Control and Driver Modeling

Abstract: Many vehicle manufacturers have lately introduced advance driver support in some of their automobiles. One of those new features is Adaptive Cruise Control DACCE, which extends the conventional cruise control system to control of relative speed and distance to other vehicles. In order to design an ACC controller it is suitable to have a model of driver behavior. The approach in the thesis is to use system identification methodology to obtain dynamic models of driver behavior useful for ACC applications. Experiment with seven drivers participating in different traffic situations were performed both on public road and on a test track. Data analysis was made by means of system identification methodology, several models of drivers longitudinal behavior being proposed, including linear regression models, subspacebased models and behavioral models. The thesis also deals with detection of when a driver is changing his behavior in various situations to a deviant behavior. To that purpose, a GARCH model was used to model the driver in situations with timevarying behavior.