Interchange flow control with dynamic obstacles optimized using genetic algorithms—a concept of virtual walls

Abstract

In the near future, autonomous vehicles will be able to share information with other vehicles via communication, enabling appropriate traffic control approaches. A new approach to traffic control using fully autonomous driving involves the realization of a flat interchange. Unlike conventional approaches, this study focused on roads and interchanges that do not assume lanes. Specifically, we propose an interchange flow control approach to traffic control using a virtual wall (VW), which acquires and shares the initial position, destination, and speed of all vehicles entering an interchange in a two-dimensional space where vehicles can move freely, and then realizes appropriate control based on this information. Each vehicle individually calculates the shortest path to avoid the VW, thereby realizing a safe and rational path selection. In this study, a genetic algorithm was used to determine the location of the VW. The effectiveness of the proposed method was evaluated using simulations, and the results showed that compared to manual deployment in the roundabout form, the proposed method using VWs reduced the total path length and the number of collisions to zero. In addition, when comparing the case where VWs were deployed in common for all vehicles and the case where VWs were deployed individually for each vehicle, it was shown that the total path length was shorter when individual VWs were deployed.