Towards Cooperative Maneuver Planning in Mixed Traffic at Urban Intersections

Connected automated driving promises a significant improvement of traffic efficiency and safety on highways and in urban areas. Apart from sharing of awareness and perception information over wireless communication links, cooperative maneuver planning may facilitate active guidance of connected automated vehicles at urban intersections. Research in automatic intersection management put forth a large body of works that mostly employ rule-based or optimization-based approaches primarily in fully automated simulated environments. In this work, we present two cooperative planning approaches that are capable of handling mixed traffic, i.e., the road being shared by automated vehicles and regular vehicles driven by humans. Firstly, we propose an optimization-based planner trained on real driving data that cyclically selects the most efficient out of multiple predicted coordinated maneuvers. Additionally, we present a cooperative planning approach based on graph-based reinforcement learning, which conquers the lack of ground truth data for cooperative maneuvers. We present evaluation results of both cooperative planners in high-fidelity simulation and real-world traffic. Simulative experiments in fully automated traffic and mixed traffic show that cooperative maneuver planning leads to less delay due to interaction and a reduced number of stops. In real-world experiments with three prototype connected automated vehicles in public traffic, both planners demonstrate their ability to perform efficient cooperative maneuvers.

Fast Long-Term Multi-Scenario Prediction for Maneuver Planning at Unsignalized Intersections

Motion prediction for intelligent vehicles typically focuses on estimating the most probable future evolutions of a traffic scenario. Estimating the gap acceptance, i.e., whether a vehicle merges or crosses before another vehicle with the right of way, is often handled implicitly in the prediction. However, an infrastructure-based maneuver planning can assign artificial priorities between cooperative vehicles, so it needs to evaluate many more potential scenarios. Additionally, the prediction horizon has to be long enough to assess the impact of a maneuver. We, therefore, present a novel long-term prediction approach handling the gap acceptance estimation and the velocity prediction in two separate stages. Thereby, the behavior of regular vehicles as well as priority assignments of cooperative vehicles can be considered. We train both stages on real-world traffic observations to achieve realistic prediction results. Our method has a competitive accuracy and is fast enough to predict a multitude of scenarios in a short time, making it suitable to be used in a maneuver planning framework.

Real-Time Spatial Trajectory Planning for Urban Environments Using Dynamic Optimization

Planning trajectories for automated vehicles in urban environments requires methods with high generality, long planning horizons, and fast update rates. Using a path-velocity decomposition, we contribute a novel planning framework, which generates foresighted trajectories and can handle a wide variety of state and control constraints effectively. In contrast to related work, the proposed optimal control problems are formulated over space rather than time. This spatial formulation decouples environmental constraints from the optimization variables, which allows the application of simple, yet efficient shooting methods. To this end, we present a tailored solution strategy based on ILQR, in the Augmented Lagrangian framework, to rapidly minimize the trajectory objective costs, even under infeasible initial solutions. Evaluations in simulation and on a full-sized automated vehicle in real-world urban traffic show the real-time capability and versatility of the proposed approach.