Autonomous Behavioral Decision for Vehicular Agents Based on Cyber-Physical Social Intelligence
Bibliographic Data
| ID | 22107384 |
|---|---|
| Authors | Zhiwei Guo (0000-0002-8973-307X, Chongqing Technology and Business University), Dian Meng (0000-0002-3004-6523, Chongqing Technology and Business University), Chinmay Chakraborty (0000-0002-4385-0975, Birla Institute of Technology, Mesra), Xing-Rong Fan (0000-0003-4561-3364, Chongqing Technology and Business University), Arpit Bhardwaj (0000-0001-7447-301X, BML Munjal University), Keping Yu (0000-0001-5735-2507, Hosei University) |
| Year | 2023 |
| Volume | 10 |
| Issue | 4 |
| Pages | 2111-2122 |
| Publication date | 2023-08-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | IEEE Transactions on Computational Social Systems (JOURNAL) |
| Journal identifiers | ISSN: 2329-924X • E-ISSN: 2373-7476 |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) (PUBLISHER) |
| DOI | 10.1109/tcss.2022.3212864 |
| OpenAlex | W4312890993 |
| Language | EN |
| Citations received | 1 |
| References cited | 31 |
In future smart cities supported by cyber-physical social intelligence, autonomous behavioral decision for vehicular agents is going to become a general demand. Despite much progress achieved in autonomous behavioral decision of vehicular agents, the existing works can just be used in scenarios of short-distance behavioral decision. Naturally, they are not well suitable for long-distance behavioral decision tasks, posing much challenge in realistic cyber-physical environment. To bridge the existing gaps, this article proposes an autonomous behavioral decision framework for vehicular agents using cyber-physical social intelligence. First, it is expected to establish a dynamic planning model with multiple objectives and constraints. This can be embedded into the control unit of a vehicular agent to endow it with proper social intelligence. On this basis, an iterative search algorithm is specifically designed for it to find the optimal solutions from the whole solution space. Finally, two typical situation cases are implemented with use of simulation modeling to display the working architecture of the proposed method. In addition, a universal optimization search algorithm is selected as the baseline to be compared with the proposed method. The comparison results reveal both planning utility and running efficiency of the proposed method
Ambient Intelligence · Behavioral modeling · Behavioural sciences · Crowd psychology · Cyber-physical system · Decision model · Machine learning · Advanced Data and IoT Technologies · Computer Science · Privacy-Preserving Technologies in Data · Vehicular Ad Hoc Networks (VANETs · Architecture · Artificial Intelligence
| Citations per year | 1 |
|---|---|
| Citation span | 2025 - 2025 (1) |
| Highly cited | No |