Abstract
Recent years have witnessed exciting developments in our transportation system with increasingly intelligent vehicles and infrastructure. The transportation system is envisioned to be highly heterogeneous, consisting of diverse participants with mixed intelligence and connectivity. Among them, autonomous vehicles have the highest intelligence and connectivity level and could contribute greatly to the operation of the transportation system in an efficient and reliable manner. However, the current design of autonomous driving techniques is mostly concerned with the autonomous vehicle at the individual level, and the overall transportation system does not provide proactive support to autonomous driving. In fact, the increasing intelligence and connectivity in transportation could be leveraged to significantly enhance the safety and efficiency of individual vehicles and the entire system. To facilitate this, vehicles need to interact and cooperate both among themselves and with the transportation infrastructure and management. In this article, we propose the societal intelligence (SI) framework. Different from the existing multientity intelligence frameworks, SI allows for much diverse interactions among the multiple entities at different levels and is thus suitable for transportation. In addition, we also render the driving process into four functional layers and demonstrate how the social intelligence framework can adapt to these layers, respectively.
| Original language | English (US) |
|---|---|
| Article number | 9347449 |
| Pages (from-to) | 9109-9121 |
| Number of pages | 13 |
| Journal | IEEE Internet of Things Journal |
| Volume | 8 |
| Issue number | 11 |
| DOIs | |
| State | Published - Jun 1 2021 |
Bibliographical note
Funding Information:Manuscript received November 4, 2020; revised January 14, 2021; accepted January 27, 2021. Date of publication February 4, 2021; date of current version May 21, 2021. This work was supported in part by the Ministry National Key Research and Development Project under Grant 2020AAA0108101, and in part by the National Science Foundation under Grant CNS-1932413 and Grant CNS-1932139. (Corresponding author: Xiang Cheng.) Xiang Cheng is with the State Key Laboratory of Advanced Optical Communication Systems and Networks, Department of Electronics, School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China (e-mail: [email protected]).
Publisher Copyright:
© 2014 IEEE.
Keywords
- Connected autonomous vehicles
- Internet of Vehicles (IoV)
- intelligent transportation systems (ITS)
- societal intelligence (SI)
