Identification and classification of state-action clusters of car-following behavior

Abstract

This research effort aims to identify and classify state-action clusters of driver behavior. The methodology first segments and clusters car-following periods into clusters that identify a specific combination of state variables (speed, lane offset, yaw angle, range and range rate) and action variables (longitudinal acceleration, lateral acceleration, and yaw rate). The state-action clusters are then analyzed using discriminant analysis to reveal the clusters that can be identified using: (1) only state variables, (2) only action variables, and (3) both state and action variables. The sample used in this paper included ten different drivers with over 100 car-following periods each, totaling over 1500 car-following periods. In summary, the results revealed that: (1) 60% of the state-action clusters can be identified using only state variables, (2) 30% of the state-action clusters can be identified using only action variables, and 100% of the state-action clusters can be identified using both state and action variables. Also, 20% of the state-action clusters require the use of both state and action variables to be identified.