A multi-agent LLM framework automatically generates traffic simulations, a broadcast-spoofing cyberattack, and a consensus defense, reducing attack-induced travel delay by 3.3% in a five-vehicle case study.
En- hancing road safety and cybersecurity in traffic manage- ment systems: Leveraging the potential of reinforcement learning.IEEE Access, 12:9963–9975, 2024
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Exploring Traffic Simulation and Cybersecurity Strategies Using Large Language Models
A multi-agent LLM framework automatically generates traffic simulations, a broadcast-spoofing cyberattack, and a consensus defense, reducing attack-induced travel delay by 3.3% in a five-vehicle case study.