Enhancing Traffic Safety with AI and 6G: Latency Requirements and Real-Time Threat Detection

2025-03-31

AI Summary

To address high end-to-end latency and insufficient reliability in real-time emergency vehicle detection and response within intelligent connected transportation systems, this paper proposes a tightly integrated 6G–AI traffic safety framework. Methodologically, we first systematically decompose the end-to-end latency budget across perception, computation, communication, and alerting stages, and model the impact of vehicle speed and user trajectory on system reliability. Leveraging multi-source sensor fusion, we design a lightweight CNN-based detection model, synergized with 6G ultra-low-latency communications and a multimodal alert dissemination module. Experimental results demonstrate stable end-to-end latency ≤100 ms and a detection accuracy of 98.7%. This work establishes a deployable real-time service paradigm for 6G–AI–enabled traffic safety and introduces a fine-grained latency-aware design methodology.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Multi-modal Vision

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Agentic search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

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.

Problem

• Real-time detection and classification of emergency vehicles using AI and 6G
• Defining latency requirements for computational and networking components in traffic safety systems
• Evaluating impact of vehicle speed and user trajectory on system reliability

Innovation

• 6G and AI framework for traffic safety
• Real-time emergency vehicle detection
• Low-latency communication requirements