The Optimal Control Algorithm of Connected and Automated Vehicles at Roundabouts with Communication Delay
Chen Huang, Ronghui Hou
TL;DR
The paper addresses roundabout control for connected automated vehicles under wireless communication delays by developing a delay-aware, multi-layer framework that couples upper-level sequencing with a delay-aware distributed model predictive controller (DMPC) for vehicles. It introduces a delay-aware vehicle dynamics model, TTC-based safety constraints, and a multi-scale objective that optimizes both micro-level vehicle motions and macro-level traffic metrics like travel time and density. The intersection controller uses a virtual platoon and binary sequencing to optimally schedule entry, while a branch-and-bound solution handles the multi-objective optimization. Simulation results show the proposed method outperforms traditional MPC and a two-layer approach, especially at higher CAV penetration and under heavy traffic, demonstrating improvements in travel time, energy consumption, and safety metrics. These findings suggest the approach can enhance roundabout throughput and stability in mixed-traffic scenarios with communication delays.
Abstract
Connected and automated vehicles (CAVs) rely on wireless communication to exchange state information for distributed control, making communication delays a critical factor that can affect vehicle motion and degrade control performance, particularly in high-speed scenarios. To address these challenges in the complex environment of roundabout intersections, this paper proposes a roundabout control algorithm, which takes into account the uncertainty of interactive information caused by time delays. First, to maintain the required distance between the current vehicle and its preceding and following vehicles, conflicting vehicles are identified based on the time-to-collision (TTC) in the conflict zone. To fully consider communication performance, a vehicle motion model incorporating time delays is established. According to the distributed model predictive control (DMPC) mechanism, the vehicle motion control that satisfies the roundabout constraints is determined. Second, by scheduling the sequence of vehicles entering the roundabout, a multiscale optimization objective is developed by integrating vehicle motion indicators and roundabout system indicators. Traffic density and travel time are embedded into the optimization problem to guide vehicles to enter the roundabout safely and stably. Through a variety of simulation experiments, the effectiveness of the proposed control algorithm is verified by comparing its performance with that of multiple control algorithms under different autonomous vehicle penetration rates and heavy traffic load scenarios.