Scalable Cooperative Maneuvering Using Conflict Analysis: Merging in Mixed Traffic

This paper discusses scalable cooperative maneuvering using conflict analysis, where conflicts are managed between multiple vehicles in mixed-autonomy environments. Two different classes of cooperation, enabled by vehicle-to-everything (V2X) communication, are considered–status sharing and intent sharing. In status sharing, connected vehicles share their current states, such as position and velocity, while in intent sharing the information about vehicles’ future trajectories is exchanged. The scalability of conflict analysis is studied through traffic scenarios where an ego vehicle interacts with multiple remote vehicles in a safety- and time-critical manner. We show thatpairwiseconflict analysis, which decomposes a large-scale conflict management problem to multiple sequentially solvable smaller-scale problems, is a key component of scalability. Conflict analysis, while being scaled up to more complex traffic scenarios, preserves the efficient consideration of different types of V2X information, time delay effects, and flexible control design. These results are demonstrated using simulations with real traffic data.