Autonomous Navigation in Interaction-Based Environments—A Case of Non-Signalized Roundabouts

To reduce the number of collision fatalities at crossroads intersections, many countries have started replacing intersections with non-signalized roundabouts, forcing the drivers to be more situationally aware and to adapt their behaviors according to the scenario. A non-signalized roundabout adds to the autonomous vehicle planning challenge, as navigating such interaction-dependent scenarios safely, efficiently, and comfortably has been a challenge even for human drivers. Unlike traffic signal-controlled roundabouts, where the merging order is centrally controlled, driving a non-signalized roundabout requires the individual actor to make the decision to merge based on the movement of other interacting actors. Most traditional autonomous planning approaches use rule-based speed assignment for generating admissible motion trajectories, which work successfully in non-interaction-based driving scenarios. They, however, are less effective in interaction-based scenarios as they lack the necessary ability to adapt the vehicle's motion according to the evolving driving scenario. In this paper, we demonstrate an adaptive tactical behavior planner (ATBP) for an autonomous vehicle that is capable of planning human-like motion behaviors for navigating a non-signalized roundabout, combining naturalistic behavior planning and tactical decision-making algorithm. The human driving simulator experiment used to learn the behavior planning approach and the ATBP design is described in this paper.