Effect of Optimization Time-Scale on Learning-Based Cooperative Merging Control at a Nonsignalized Intersection

Abstract

Automated driving and the widespread use of large-scale communication infrastructure are expected to facilitate highly cooperative driving. Although considerable research has focused on developing efficient cooperative control methods for nonsignalized intersections, the effect of cooperative control for conflicting target vehicles on future traffic flow is yet to be investigated. Therefore, we aim to investigate whether the impact of such cooperative control on future traffic should be considered. We established a traffic simulator and several machine-learning methods to select the optimal cooperative method. The decision tree and deep neural network were trained on two indices that evaluate short-term/long-term predictive control: to minimize the travel time of the (1) conflicting vehicles and (2) all vehicles including future traffic flow. Simulation analysis results indicated that there were no significant differences in the total travel times between these indices. This finding indicates that efficient traffic flow, which includes future traffic flow, is achievable by short-term cooperative control methods that can be established easily.