Abstract
Connected autonomous vehicles can leverage communication and artificial intelligence technologies to effectively overcome the perceived limitations of individuals and enhance driving safety and stability. However, due to the high dynamics of the vehicular network and frequent interruptions and handovers, it is still challenging to provide stable communication connections between vehicles, which is likely to cause disasters. To address this issue, in this paper, we propose an intelligent clustering mechanism based on driving patterns in heterogeneous Cognitive Internet of Vehicles (CIoVs). In the proposed approach, we analyze the driving mode containing multiple feature parameters to accurately capture the driving characteristics. To ensure the accuracy of pattern recognition, a genetic algorithm-based neural network pattern recognition algorithm is proposed to support the reliable clustering of connected autonomous vehicles. The cognitive engines recognize the driving modes to group vehicles with a similar driving mode into a relatively stable cluster. In addition, we formulate the stability and survival time of clusters and analyze the communication performance of the clustering mechanism. Simulation results show that the proposed mechanism improves the reliable communication throughput and average cluster lifetime by approximately 14.4% and 11.5% respectively compared to the state-of-the-art approaches.
Highlights
The Intelligent Transportation System (ITS) benefits from communication and intelligence technologies to enhance traffic management capabilities and improve vehicle driving efficiency
We describe the process of modelling Autonomous driving (AD) patterns and build a training model which serves as the basis for pattern recognition
We mainly discuss the accuracy of the AD pattern recognition and the reliability of cluster communication to evaluate our proposed architecture and clustering mechanism
Summary
The Intelligent Transportation System (ITS) benefits from communication and intelligence technologies to enhance traffic management capabilities and improve vehicle driving efficiency. It is difficult to guarantee a continuous and consistent communication service, which is essential for the security of network-assisted AD [12,13,14] To address these issues, in this paper, we propose an intelligent clustering mechanism based on driving behaviors in heterogeneous CIoVs, which introduces CE to recognize. The proposed mechanism allows for more stable clusters through CAVs collaboration, reduces communication interruptions and switching caused by random movements, and ensures communication connectivity and reliability. We design a heterogeneous CIoVs network architecture and propose an intelligent clustering mechanism using AD pattern recognition, which brings CAVs with the same or a similar driving mode together to form a stable cluster to enhance the connectivity of the communication service.
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