| Autor: |
Aydin Zaboli, Junho Hong, Jaerock Kwon, John Moore |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
IEEE Access, Vol 11, Pp 136706-136725 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2023.3338156 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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