Abnormal driving presents a significant risk to road safety, impacting both drivers and pedestrians. Meanwhile, vehicular networking has emerged as a crucial technology for enhancing road safety by improving vehicle-to-infrastructure communication. This study leverages vehicular network communications to identify dangerous driving behaviors, such as aggressive or impaired driving. We employ graph-based methods to analyze traffic patterns via network topology, known for its clarity, scalability, and broad applicability. By utilizing cooperative awareness messages (CAMs) alongside a synthetic dataset, we map the topological relationships between vehicles. Our approach uses supervised Graph Neural Networks (GNNs) — specifically Graph Convolutional Network (GCN), Graph Attention Network (GAT), and Graph Sample and Aggregate (GraphSAGE) — to accurately detect abnormal driving behaviors. The experimental results demonstrate that GraphSAGE outperforms GCN and GAT, achieving near-optimal performance with high recall, precision, and F1 scores in identifying these behaviors.
Utilizing Graph Neural Networks to Detect Abnormal Driving Patterns in Vehicular Networks
28.10.2024
827464 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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