Scalability of Platoon-based Coordination for Mixed Autonomy Intersections

Abstract

As transportation systems see gradual deployment of connected and automated vehicles (CAVs), there is increasing opportunity for intelligent coordination of CAVs towards system-wide objectives. While numerous previous works have modeled single junctions (e.g. intersections and merges) and investigated control theory-based strategies for vehicle-based coordination, this work investigates the scalability of vehicular control approaches to large networks of intersections, where interactions among multiple intersections may amplify traffic disturbances. Moreover, this work focuses on mixed autonomy networks where the highly nonlinear behavior of human-driven vehicles (HDVs) complicate overall system dynamics, and where the formation of CAV-led platoons may be advantageous. Two approaches are considered for the studied settings: model predictive control (MPC) and model-free reinforcement learning (RL), both adapted from previous methods designed for single intersection and/or full autonomy settings. Results in a network of two intersections demonstrate that MPC faces significant challenges in low-level nonlinear trajectory optimization as well as high-level crossing scheduling, while the RL policies implicitly optimizes for both low-level control and high-level coordination. Scalability analysis in large networks with hundred of intersections reveal that policies derived from additional finetuning only suffer mild degradation in performance despite the numerous out-of-distribution traffic conditions that may emerge under large scale.