A Curvilinear Decision Method for Two-lane Roundabout Crossing and its Validation under Realistic Traffic Flow

Autonomous vehicles navigation in complex scenarios is still an open issue. One of the major challenges is the safe navigation of autonomous vehicles on roads open to public traffic. Indeed, behaviors and intentions of human-driven vehicles are hard to predict and understand. In this paper, we propose a strategy to make an autonomous vehicle able to cross safely a roundabout. Our approach relies on High-Definition (HD) maps with lane level description which allows to predict the future situation thanks to the concept of virtual vehicles. This method handles safely collision avoidance and guarantees that no priority constraint is violated during the insertion maneuver without being overly cautious. The performance is evaluated with the SUMO simulation framework. An highly interactive vehicles flow has been generated using real data from the INTERACTION dataset. We also propose strategies to extend our algorithm to multi-lane roundabouts and report how these extensions behave in terms of safety and traffic flow.