research-article

Reality Bites: Assessing the Realism of Driving Scenarios with Large Language Models

Authors:

Shaukat AliAuthors Info & Claims

FORGE '24: Proceedings of the 2024 IEEE/ACM First International Conference on AI Foundation Models and Software Engineering

Pages 40 - 51

https://doi.org/10.1145/3650105.3652296

Published: 12 June 2024 Publication History

Abstract

Large Language Models (LLMs) are demonstrating outstanding potential for tasks such as text generation, summarization, and classification. Given that such models are trained on a humongous amount of online knowledge, we hypothesize that LLMs can assess whether driving scenarios generated by autonomous driving testing techniques are realistic, i.e., being aligned with real-world driving conditions. To test this hypothesis, we conducted an empirical evaluation to assess whether LLMs are effective and robust in performing the task. This reality check is an important step towards devising LLM-based autonomous driving testing techniques. For our empirical evaluation, we selected 64 realistic scenarios from DeepScenario-an open driving scenario dataset. Next, by introducing minor changes to them, we created 512 additional realistic scenarios, to form an overall dataset of 576 scenarios. With this dataset, we evaluated three LLMs (GPT-3.5, Llama2-13B, and Mistral-7B) to assess their robustness in assessing the realism of driving scenarios. Our results show that: (1) Overall, GPT-3.5 achieved the highest robustness compared to Llama2-13B and Mistral-7B, consistently throughout almost all scenarios, roads, and weather conditions; (2) Mistral-7B performed the worst consistently; (3) Llama2-13B achieved good results under certain conditions; and (4) roads and weather conditions do influence the robustness of the LLMs.

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Cited By

Ali SArcaini PArrieta A(2024)Foundation Models for the Digital Twins Creation of Cyber-Physical SystemsLeveraging Applications of Formal Methods, Verification and Validation. Application Areas10.1007/978-3-031-75390-9_2(9-26)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1007/978-3-031-75390-9_2

Index Terms

Reality Bites: Assessing the Realism of Driving Scenarios with Large Language Models

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cover image ACM Conferences

FORGE '24: Proceedings of the 2024 IEEE/ACM First International Conference on AI Foundation Models and Software Engineering

April 2024

140 pages

ISBN:9798400706097

DOI:10.1145/3650105

Chair:
David Lo,
Co-chair:
Xin Xia,
Program Chairs:
Massimiliano Di Penta,
Xing Hu
Zhejiang University, China

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Published: 12 June 2024

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Norges Forskningsråd
Department of Education, Universities and Research of the Basque Country

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FORGE '24: 2024 IEEE/ACM First International Conference on AI Foundation Models and Software Engineering

April 14, 2024

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Cited By

Ali SArcaini PArrieta A(2024)Foundation Models for the Digital Twins Creation of Cyber-Physical SystemsLeveraging Applications of Formal Methods, Verification and Validation. Application Areas10.1007/978-3-031-75390-9_2(9-26)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1007/978-3-031-75390-9_2

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