Towards Context-Awareness for Enhanced Safety of Autonomous Vehicles

Haupt, Nikita Bhardwaj; Liggesmeyer, Peter

doi:10.1007/978-3-030-82196-8_40

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A System Architecture Of Autonomous Vehicles1

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2022

2023

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Cited by 2 publications

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“…2. The CXI without consideration of safety is ignored since not all information concerning EOCs is crucial to the AV's security [47], [50], [51].…”

Section: A System Architecture Of Autonomous Vehiclesmentioning

confidence: 99%

A Survey on Cyber-Physical Security of Autonomous Vehicles Using a Context Awareness Method

Zaboli,

Hong,

Kwon

et al. 2023

IEEE Access

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Autonomous vehicles face challenges in ensuring cyber-physical security due to their reliance on image data from cameras processed by machine learning. These algorithms, however, are vulnerable to anomalies in the imagery, leading to decreased recognition accuracy and presenting security concerns. Current machine learning models struggle to predict unexpected vehicular situations, particularly with unpredictable objects and unexpected anomalies. To combat this, scholars are focusing on active inference, a method that can adapt models based on human cognition. This paper aims to incorporate active inference into autonomous vehicle systems. Multiple studies have delved into this approach, showing its potential to address security gaps in this field. Specifically, these frameworks have proven effective in handling unforeseen vehicular anomalies.

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“…2. The CXI without consideration of safety is ignored since not all information concerning EOCs is crucial to the AV's security [47], [50], [51].…”

Section: A System Architecture Of Autonomous Vehiclesmentioning

confidence: 99%