LASER: Script Execution by Autonomous Agents for On-demand Traffic Simulation
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909362611552256 |
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| author | Gao, Hao Wang, Jingyue Fang, Wenyang Xu, Jingwei Huang, Yunpeng Chen, Taolue Ma, Xiaoxing |
| author_facet | Gao, Hao Wang, Jingyue Fang, Wenyang Xu, Jingwei Huang, Yunpeng Chen, Taolue Ma, Xiaoxing |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_16197 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LASER: Script Execution by Autonomous Agents for On-demand Traffic Simulation Gao, Hao Wang, Jingyue Fang, Wenyang Xu, Jingwei Huang, Yunpeng Chen, Taolue Ma, Xiaoxing Robotics Multiagent Systems 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. |
| title | LASER: Script Execution by Autonomous Agents for On-demand Traffic Simulation |
| topic | Robotics Multiagent Systems |
| url | https://arxiv.org/abs/2410.16197 |