I'm Sorry Driver, I'm Afraid I Can't Do That: Appraising the Safety of LLMs within Automotive Contexts
arXiv:2606.14327v1 Announce Type: cross Abstract: This paper appraises recent frameworks within AI development to integrate LLMs into control tasks in automotive contexts from the perspective of safety assurance. This work has built upon the rapid integration of LLMs across automotive settings. However, we find that at present, these frameworks face significant challenges, limiting their efficacy in real-time safety-critical contexts. Firstly, we consider conceptual challenges, including the fact that deployers are faced with a dual challenge, wherein they must assure a model which has been de
The rapid integration of LLMs across various sectors, including automotive, necessitates immediate appraisal of their safety given their increasing deployment in real-world control tasks.
The safety of LLMs in critical applications like autonomous vehicles is paramount, directly influencing public trust, regulatory frameworks, and the pace of AI adoption in highly sensitive sectors.
This research highlights critical challenges to the efficacy and safety assurance of LLM frameworks in automotive contexts, pushing for more robust and secure integration methodologies.
- · AI safety researchers
- · Automotive safety testing companies
- · Developers of robust AI verification tools
- · Companies rushing LLM deployment without adequate safety measures
- · Consumers of unsafe AI-driven automotive features
Increased scrutiny and demand for certified AI safety standards in automotive and other safety-critical industries.
Slower adoption rates of LLM-driven control systems in vehicles until more rigorous safety protocols and assurance methods are established.
The development of a specialized sub-industry focused on AI safety assurance and certification for critical infrastructure and autonomous systems globally.
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Read at arXiv cs.AI