Cooperative Maneuver Planning for Mixed Traffic at Unsignalized Intersections Using Probabilistic Predictions

Intersections are among the scenarios that are most crucial for efficiency and traffic flow on roads. Several approaches to traffic control at intersections exist, each with its own advantages and drawbacks. These days, wireless connections between road users, automated vehicles, and intelligent infrastructure enable new ways of coordinating traffic. However, the gradual deployment of those advanced technologies leads to a heterogeneous mixture of partially automated, connected, and legacy vehicles. Planning and coordinating maneuvers for this mixed traffic is a challenge and subject to current research, as it can achieve significant efficiency improvements in those scenarios. In this paper, we propose a new maneuver planning system for cooperative connected vehicles in mixed traffic at unsignalized intersections, which often occur in urban areas. Our system consists of a probabilistic multi-modal prediction based on a driver model and an efficient optimization algorithm to find the best maneuvers. We present the functionality of our approach and evaluate the impact on traffic efficiency using simulations of two different intersection layouts at various rates of cooperative vehicle penetration.

Automated summary

This paper proposes a new maneuver planning system for cooperative connected vehicles in mixed traffic at unsignalized intersections to improve traffic efficiency. The system utilizes probabilistic multi-modal prediction and an optimization algorithm to find the best maneuvers, suitable for unsignalized intersections in urban areas.