Mind the Gap: Evaluating LLM Understanding of Human-Taught Road Safety Principles

📄 Abstract

Following road safety norms is non-negotiable not only for humans but also for the AI systems that govern autonomous vehicles. In this work, we evaluate how well multi-modal large language models (LLMs) understand road safety concepts, specifically through schematic and illustrative representations. We curate a pilot dataset of images depicting traffic signs and road-safety norms sourced from school text books and use it to evaluate models capabilities in a zero-shot setting. Our preliminary results show that these models struggle with safety reasoning and reveal gaps between human learning and model interpretation. We further provide an analysis of these performance gaps for future research.

🔍 Key Points

Evaluation of multi-modal LLMs shows gaps in understanding human-taught road safety principles, specifically in reasoning and abstraction.
A novel Road Safety Understanding Task was developed, categorized into Traffic-Sign Detection, Road-Safety Hazard, and Hazard-Pair Comparison.
Preliminary results reveal high accuracy for traffic sign identification but significant struggles with comparative reasoning in road safety scenarios.
Identified specific error patterns highlight the need for improved training approaches to strengthen models' contextual understanding of safety norms.
Study emphasizes the importance of aligning AI systems with human learning paradigms to foster safer autonomous vehicle operations.

💡 Why This Paper Matters

This paper is essential as it highlights the limitations of current AI models to comprehend and reason about fundamental road safety principles as humans do. The insights gained from evaluating large language models in this domain are critical for developing intelligent transportation systems that are aligned with human safety standards. By bridging the gap between AI understanding and human learning, this work lays the groundwork for future research that could lead to more reliable and context-aware autonomous driving systems.

🎯 Why It's Interesting for AI Security Researchers

For AI security researchers, this paper draws attention to the vulnerabilities in AI systems related to human safety norms, particularly in the context of traffic and road safety. Understanding how AI interprets safety information is paramount for developing AI systems that can operate safely in real-world scenarios. The gaps identified in the models' reasoning highlight potential areas for adversarial attacks or misinterpretations, which could lead to hazardous situations. Thus, this research is vital for informing security measures in deploying LLMs in practical applications where safety is paramount.

Mind the Gap: Evaluating LLM Understanding of Human-Taught Road Safety Principles

📄 Abstract

🔍 Key Points

💡 Why This Paper Matters

🎯 Why It's Interesting for AI Security Researchers

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