Leveraging Multiagent Learning for Automated Vehicles Scheduling at Nonsignalized Intersections
| Autor: | Jiwei Zhao, Ting Ma, Yunting Xu, Xuemin Shen, Bo Qian, Haibo Zhou |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Collision avoidance (spacecraft)
Artificial neural network Computer Networks and Communications Computer science Distributed computing Reliability (computer networking) 020206 networking & telecommunications Throughput 02 engineering and technology Computer Science Applications Scheduling (computing) Intersection Hardware and Architecture Signal Processing 0202 electrical engineering electronic engineering information engineering Reinforcement learning 020201 artificial intelligence & image processing Information exchange Information Systems |
| Zdroj: | IEEE Internet of Things Journal. 8:11427-11439 |
| ISSN: | 2372-2541 |
| DOI: | 10.1109/jiot.2021.3054649 |
| Popis: | Recent advancements of Vehicle-to-Everything (V2X) communication combined with artificial intelligence (AI) technologies have shown enormous potentials for improving traffic management efficiency and intelligence. To provide innovative and effective data-driven traffic management solution for the coming automated vehicle era, we present a vehicle–road collaboration-enabled nonsignalized intersection management architecture in this paper. First, by dividing the intersection zone into the central section (CS) and the waiting section (WS), a vehicle regulation scheme involved with communication and computation planes is developed for V2X-enabled nonsignalized intersection management. Specifically, in order to guarantee vehicle safety, the definition of no overlapping occupation time in CS and the fastest crossing time point (FCTP) algorithm are employed for vehicle collision avoidance. Second, considering the relative coordination between adjacent intersections, a multiagent-based deep reinforcement learning scheduling (MA-DRLS) algorithm is proposed to realize cooperative multiple intersection management. Through information exchange with different intersection agents, each agent can obtain an optimal scheduling strategy using independent deep reinforcement learning (DRL) network. The features of fixed $Q$ -targets and experience replay are leveraged to improve the reliability of neural network during the training process. Finally, simulation performances in terms of intersection throughput and vehicle waiting time have been provided to validate the effectiveness and demonstrate the superiority of the proposed nonsignalized intersection management solution. |
| Databáze: | OpenAIRE |
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