Name
Abstract | ||
|---|---|---|
Intelligent driving plays a role in significantly improving the safety and efficiency of transportation systems. As the onboard capabilities of perception, comprehension, and decision making are limited, vehicles can employ the edge computing infrastructure of the smart road to enhance their intelligence. Therefore, the smart road is considered an intelligent Internet of Things system. It provides vehicles with not only the road space in the transportation domain, but also the communication, sensing, and computing resources in the information domain to improve the composite quality of intelligent driving. However, the resources in the information and transportation domains are complicatedly coupled, and the orchestration of these cross-domain resources is confronted with the huge state-action space, which cannot be solved in a real-time manner. In this article, we investigate the fundamental research challenges in cross-domain resource orchestration for the smart road, and design a multi-agent-based framework. Within the framework, each vehicle is associated with an exclusive agent on the edge cloud, and the agents utilize swarm intelligence to jointly optimize the traffic flow and information flow for their respective vehicles. Specifically, a value iteration network is used by agents to learn the routing behavior of vehicles, and a multi-agent deep reinforcement learning method is proposed, enabling agents to cooperatively learn decentralized resource optimization policies. To verify the effectiveness of the proposed framework, a cross-domain resource orchestration prototype is implemented and evaluated. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/MNET.011.2000007 | IEEE Network |
Keywords | DocType | Volume |
Roads,Resource management,Routing,Edge computing,Optimization,Planning | Journal | 34 |
Issue | ISSN | Citations |
5 | 0890-8044 | 4 |
PageRank | References | Authors |
0.37 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Quan Yuan | 1 | 55 | 11.07 |
| Jinglin Li | 2 | 150 | 30.39 |
| haibo zhou | 3 | 61 | 9.11 |
| Guiyang Luo | 4 | 23 | 4.35 |
| Tao Lin | 5 | 12 | 0.83 |
| Fangchun Yang | 6 | 1082 | 90.49 |
| Xuemin Sherman Shen | 7 | 13 | 6.97 |