Hidden Risks of Unmonitored GPUs in Intelligent Transportation Systems
🤖 AI Summary
This study addresses a critical yet overlooked security vulnerability in intelligent transportation systems: the lack of effective GPU monitoring, which can lead to silent degradation of real-time performance and safety hazards due to GPU misuse. For the first time, this work identifies GPU abuse as a safety blind spot in this domain and reveals its subtle yet significant threat to system timeliness. By conducting system-level performance monitoring and analyzing GPU workload characteristics, the research quantifies the adverse impact of unmanaged GPU tasks on real-time responsiveness. The findings demonstrate that insufficient GPU oversight substantially undermines real-time performance, offering a novel perspective and empirical foundation for enhancing the safety architecture of intelligent transportation systems.
Application Category
📝 Abstract
Graphics processing units (GPUs) power many intelligent transportation systems (ITS) and automated driving applications, but remain largely unmonitored for safety and security. This article highlights GPU misuse as a critical blind spot, showing how unmanaged GPU workloads silently degrade real-time performance, demonstrating the need for stronger security measures in ITS.
Problem
Research questions and friction points this paper is trying to address.
GPU misuse
intelligent transportation systems
real-time performance degradation
unmonitored GPUs
safety and security
Innovation
Methods, ideas, or system contributions that make the work stand out.
GPU security
intelligent transportation systems
real-time performance degradation
unmonitored GPU workloads
automated driving safety
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