A Consensus Resistance-Based Autonomous Vehicle Social Group Self-Adaption Method
Bibliographic Data
| ID | 22107804 |
|---|---|
| Authors | Jiujun Cheng (0000-0001-5176-4762, Tongji University), Lu Yang (0000-0002-2623-6789, Tongji University), Zhangkai Ni (0000-0003-3682-6288, Tongji University), Yuhong Li (0000-0003-0056-8862, Stockholm University), Guangtao Zhou (0009-0009-0075-9256, China United Network Communications Group (China)), Zhenhua Huang (0000-0001-5990-8425, South China Normal University), Shangce Gao (0000-0001-5042-3261, University of Toyama) |
| Year | 2026 |
| Volume | 13 |
| Issue | 2 |
| Pages | 1734-1744 |
| Publication date | 2026-04-01 |
| 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.2025.3623147 |
| OpenAlex | W4417438770 |
| Language | EN |
| References cited | 35 |
The advancement of autonomous driving technology has brought significant benefits to modern transportation systems. However, individual autonomous vehicles face challenges such as limited perception range and insufficient autonomous capabilities. Cooperative groups of autonomous vehicles, enabled by advanced communication technologies, can enhance traffic efficiency through information exchange. Existing research primarily focuses on centralized autonomous vehicle groups, where the leading node suffers from weak resilience and high computational load, making it difficult to maintain group collaboration over time. To address these issues, this article proposes a decentralized formation and self-adaptation method for autonomous vehicle social groups based on consensus resistance in closed scenes. First, we introduce consensus resistance as a metric to evaluate social group and member consistency, and develop a decentralized formation approach. Second, we present a self-adaption model for autonomous vehicle social groups, incorporating four evolutionary events: 1) expansion; 2) merging; 3) reduction; and 4) splitting, to ensure the stability of moving social groups. Simulation results demonstrate the proposed method effectively constructs social groups in both real-world and simulated environments, exhibiting robust consistency throughout the self-adaption process
Perception · Psychological resilience · Social group · Autonomous Vehicle Technology and Safety · Traffic control and management · Vehicular Ad Hoc Networks (VANETs
| Highly cited | No |
|---|