Enhancing Traffic Safety with AI and 6G: Latency Requirements and Real-Time Threat Detection
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911277099515904 |
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| author | Horvath, Kurt Kimovski, Dragi Kitanov, Stojan Prodan, Radu |
| author_facet | Horvath, Kurt Kimovski, Dragi Kitanov, Stojan Prodan, Radu |
| contents | The rapid digitalization of urban infrastructure opens the path to smart cities, where IoT-enabled infrastructure enhances public safety and efficiency. This paper presents a 6G and AI-enabled framework for traffic safety enhancement, focusing on real-time detection and classification of emergency vehicles and leveraging 6G as the latest global communication standard. The system integrates sensor data acquisition, convolutional neural network-based threat detection, and user alert dissemination through various software modules of the use case. We define the latency requirements for such a system, segmenting the end-to-end latency into computational and networking components. Our empirical evaluation demonstrates the impact of vehicle speed and user trajectory on system reliability. The results provide insights for network operators and smart city service providers, emphasizing the critical role of low-latency communication and how networks can enable relevant services for traffic safety. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_24143 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enhancing Traffic Safety with AI and 6G: Latency Requirements and Real-Time Threat Detection Horvath, Kurt Kimovski, Dragi Kitanov, Stojan Prodan, Radu Distributed, Parallel, and Cluster Computing The rapid digitalization of urban infrastructure opens the path to smart cities, where IoT-enabled infrastructure enhances public safety and efficiency. This paper presents a 6G and AI-enabled framework for traffic safety enhancement, focusing on real-time detection and classification of emergency vehicles and leveraging 6G as the latest global communication standard. The system integrates sensor data acquisition, convolutional neural network-based threat detection, and user alert dissemination through various software modules of the use case. We define the latency requirements for such a system, segmenting the end-to-end latency into computational and networking components. Our empirical evaluation demonstrates the impact of vehicle speed and user trajectory on system reliability. The results provide insights for network operators and smart city service providers, emphasizing the critical role of low-latency communication and how networks can enable relevant services for traffic safety. |
| title | Enhancing Traffic Safety with AI and 6G: Latency Requirements and Real-Time Threat Detection |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2503.24143 |