Engineering Safety Requirements for Autonomous Driving with Large Language Models

Core Concepts

Large Language Models can effectively support human engineers in specifying safety requirements for Autonomous Driving functions.

Abstract

The content discusses the use of Large Language Models (LLMs) to automate the process of refining and decomposing safety requirements for Autonomous Driving (AD) functions. The study proposes a prototype pipeline that utilizes prompts and LLMs to generate safety requirements, review requirement datasets, and identify redundant or contradictory requirements. The methodology involves iterative design cycles focusing on identifying limitations, task breakdown, prompt engineering, and real-world evaluation by experts. The results show promising capabilities of LLMs in automating safety requirement specifications for AD functions.
Directory:

Introduction

Challenges in ensuring safety for AD software.

Hazard Analysis and Risk Assessment (HARA)

Importance of HARA in automotive domain.

Prototype Design Cycles

Iterative design process focusing on LLM limitations, task breakdown, prompt engineering.

Evaluation by Experts

Real-world evaluation of LLM-generated HARA.

Conclusion and Further Work

Potential applications and future research directions.

Stats

"The tool not only demonstrates efficiency, completing tasks in one day compared to the months required by human effort."
"The findings show that while LLMs are capable of providing definitions and reason about safety-critical software systems."

Quotes

"in the future, it will be a powerful tool that might exceed human competence."
"[...] can be a complement but hard to replace current way of doing"

Key Insights Distilled From

Engineering Safety Requirements for Autonomous Driving with Large Language Models

by Ali ... at arxiv.org 03-26-2024

https://arxiv.org/pdf/2403.16289.pdf

Deeper Inquiries

How can the use of LLMs impact the efficiency of DevOps cycles?

The use of Large Language Models (LLMs) can significantly impact the efficiency of DevOps cycles in various ways. Firstly, LLMs can automate tasks that are traditionally time-consuming and labor-intensive, such as generating safety requirements for autonomous driving functions. This automation reduces manual effort and speeds up the process, allowing teams to iterate more quickly through development stages. Additionally, LLMs can assist in brainstorming about possible hazards and scenarios, providing valuable insights that human engineers might overlook. By automating these tasks, LLMs free up human resources to focus on higher-level decision-making and strategic planning within the DevOps cycle.

What are the potential risks associated with relying solely on AI tools like LLMs for critical safety activities?

Relying solely on AI tools like Large Language Models (LLMs) for critical safety activities poses several potential risks that need to be carefully considered. One significant risk is related to interpretability and explainability - since LLMs operate based on complex algorithms and vast amounts of data, their decision-making processes may not always be transparent or easily understandable by humans. This lack of transparency could lead to challenges in verifying the accuracy and reliability of outputs generated by LLMs for critical safety activities.
Another risk is related to bias inherent in AI models - if an LLM is trained on biased data or flawed assumptions, it may inadvertently perpetuate biases in its outputs when used for critical safety tasks. This could result in incorrect or unfair decisions being made based on biased information provided by the AI tool.
Furthermore, there is a risk of overreliance on AI tools leading to complacency among human operators who may trust the output from an LLM without conducting thorough verification checks themselves. In critical safety activities where lives are at stake, it's crucial to maintain a balance between leveraging AI technology for efficiency gains while ensuring human oversight and intervention when necessary.

How can prompt engineering techniques be further improved to enhance the performance of LLMs in generating safety requirements?

Prompt engineering techniques play a vital role in enhancing the performance of Large Language Models (LLMs) in generating safety requirements effectively. To further improve prompt engineering techniques for this purpose:

Tailored Prompts: Develop prompts specifically designed for HARA tasks related to specifying safety requirements for AD functions rather than using generic prompts.
Contextual Information: Provide contextual information within prompts so that LMMs have relevant background knowledge required for accurate generation.
Feedback Loop: Implement a feedback loop mechanism where experts review generated outputs and provide corrections or suggestions which are then incorporated back into prompt design.
Examples & Templates: Include examples and templates within prompts guiding how specific types of information should be structured or presented.
5 .Explainability Guidance: Incorporate guidance within prompts instructing how explanations should accompany each output generated by an LLN; this enhances understanding during review processes.
6 .Diverse Inputs: Use diverse inputs during training phases including real-world scenarios from different automotive functions ensuring robustness across various contexts.

By implementing these improvements into prompt engineering strategies tailored towards HARA tasks involving specifying safety requirements with AD functions will likely enhance overall performance outcomes when utilizing LLNs in such critical applications."