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

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 as-sess 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 empir-ical 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 sce-narios, 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.