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.
LASER: Script Execution by Autonomous Agents for On-demand Traffic Simulation
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abstract
Autonomous Driving Systems (ADS) require diverse and safety-critical traffic scenarios for effective training and testing, but the existing data generation methods struggle to provide flexibility and scalability. We propose LASER, a novel frame-work that leverage large language models (LLMs) to conduct traffic simulations based on natural language inputs. The framework operates in two stages: it first generates scripts from user-provided descriptions and then executes them using autonomous agents in real time. Validated in the CARLA simulator, LASER successfully generates complex, on-demand driving scenarios, significantly improving ADS training and testing data generation.
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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.