Conversational Code Generation: a Case Study of Designing a Dialogue System for Generating Driving Scenarios for Testing Autonomous Vehicles
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
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| _version_ | 1866912575071977472 |
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| author | Rubavicius, Rimvydas Miceli-Barone, Antonio Valerio Lascarides, Alex Ramamoorthy, Subramanian |
| author_facet | Rubavicius, Rimvydas Miceli-Barone, Antonio Valerio Lascarides, Alex Ramamoorthy, Subramanian |
| contents | Cyber-physical systems like autonomous vehicles are tested in simulation before deployment, using domain-specific programs for scenario specification. To aid the testing of autonomous vehicles in simulation, we design a natural language interface, using an instruction-following large language model, to assist a non-coding domain expert in synthesising the desired scenarios and vehicle behaviours. We show that using it to convert utterances to the symbolic program is feasible, despite the very small training dataset. Human experiments show that dialogue is critical to successful simulation generation, leading to a 4.5 times higher success rate than a generation without engaging in extended conversation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_09829 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Conversational Code Generation: a Case Study of Designing a Dialogue System for Generating Driving Scenarios for Testing Autonomous Vehicles Rubavicius, Rimvydas Miceli-Barone, Antonio Valerio Lascarides, Alex Ramamoorthy, Subramanian Computation and Language Information Retrieval Robotics Cyber-physical systems like autonomous vehicles are tested in simulation before deployment, using domain-specific programs for scenario specification. To aid the testing of autonomous vehicles in simulation, we design a natural language interface, using an instruction-following large language model, to assist a non-coding domain expert in synthesising the desired scenarios and vehicle behaviours. We show that using it to convert utterances to the symbolic program is feasible, despite the very small training dataset. Human experiments show that dialogue is critical to successful simulation generation, leading to a 4.5 times higher success rate than a generation without engaging in extended conversation. |
| title | Conversational Code Generation: a Case Study of Designing a Dialogue System for Generating Driving Scenarios for Testing Autonomous Vehicles |
| topic | Computation and Language Information Retrieval Robotics |
| url | https://arxiv.org/abs/2410.09829 |