Assessing Realism of Driving Scenarios with Large Language Models

Core Concepts

Large Language Models can effectively assess the realism of driving scenarios, with GPT-3.5 showing the highest robustness.

Abstract

The study evaluates the ability of Large Language Models (LLMs) like GPT-3.5, Llama2-13B, and Mistral-7B to assess the realism of driving scenarios. By testing 576 scenarios, it was found that GPT-3.5 exhibited the highest robustness overall, followed by Llama2-13B and Mistral-7B. The research highlights the importance of using LLMs in autonomous driving testing techniques to generate realistic scenarios efficiently.

Stats

GPT-3.5 achieved a robustness score of 12.59 out of 20. Mistral-7B had a robustness score of 5.60. Llama2-13B scored 9.48 in terms of robustness.

Quotes

"Large Language Models have great potential in assessing the realism of driving scenarios." "GPT-3.5 demonstrated the highest robustness compared to other models." "Mistral-7B consistently performed the worst in assessing scenario realism."

Key Insights Distilled From

Reality Bites

by Jiahui Wu,Ch... at arxiv.org 03-18-2024

https://arxiv.org/pdf/2403.09906.pdf

Deeper Inquiries

How can LLMs be further optimized for assessing complex driving scenarios?

LLMs can be further optimized for assessing complex driving scenarios by fine-tuning them on specific datasets related to autonomous driving. This process involves training the models on a large amount of diverse and realistic driving data to improve their understanding of different road conditions, weather patterns, and vehicle interactions. Additionally, incorporating domain-specific knowledge into the pre-training process can enhance the models' ability to accurately assess the realism of driving scenarios. Furthermore, optimizing the prompt design and input format tailored specifically for evaluating driving scenarios can also improve the performance of LLMs in this context.

What are potential limitations or biases in using LLMs for evaluating driving scenarios?

One potential limitation is that LLMs may not have access to real-time data from sensors and cameras like actual autonomous vehicles do. This lack of real-time information could lead to inaccuracies in assessing dynamic situations on the road. Additionally, biases present in the training data used to pre-train LLMs could impact their evaluation of driving scenarios. For example, if certain types of vehicles or road conditions are underrepresented in the training data, it could result in biased assessments by the model.

How might advancements in LLM technology impact autonomous vehicle development beyond scenario assessment?

Advancements in LLM technology could have far-reaching implications for autonomous vehicle development beyond scenario assessment. These advancements could lead to improved decision-making capabilities for autonomous vehicles by enhancing their understanding of complex natural language instructions and environmental cues. Additionally, more sophisticated LLMs could enable better communication between autonomous vehicles and human drivers or pedestrians through natural language interfaces. Moreover, advanced LLMs could facilitate faster adaptation to new environments and unforeseen circumstances on the road, ultimately enhancing overall safety and efficiency in autonomous vehicle operations.