Proactive Motion Planning for Uncontrolled Blind Intersections to Improve the Safety and Traffic Efficiency of Autonomous Vehicles

For the last two decades, autonomous vehicles have been proposed and developed to extend the operational design domain from the motorway to urban environments. However, there have been few studies on autonomous driving for uncontrolled and blind intersections. This paper presents a proactive motion planning algorithm to enhance safety and traffic efficiency simultaneously for autonomous driving in uncontrolled blind intersections. The target states of approach motion are decided based on the field of view of the laser scanner and the pre-defined intersection map with connectivity information. The model predictive controller is used to follow the target states and determine the longitudinal motion of an autonomous vehicle. A Monte Carlo simulation with a case study was conducted to evaluate the performance of the proposed proactive motion planner. The simulation results show that the risk caused by approaching vehicles from the occluded region is properly managed. In addition, the traffic flow is improved by reducing the required time to cross the intersections.