التفاصيل البيبلوغرافية
| العنوان: |
EVENT: Real-time Video Feed Anomaly Detection for Enhanced Security in Autonomous Vehicles |
| المؤلفون: |
Aivatoglou, Georgios, Oikonomou, Nikolaos, Spanos, Georgios, Livitckaia, Kristina, Votis, Konstantinos, Tzovaras, Dimitrios |
| المصدر: |
MED, 2023 31st Mediterranean Conference on Control and Automation, Limassol, Cyprus, 26-29 June 2023 |
| بيانات النشر: |
IEEE |
| سنة النشر: |
2023 |
| المجموعة: |
Zenodo |
| الوصف: |
Autonomous Vehicles have long leveraged Artilicial Intelligence to be capable of self-driving without the need for a human supervisor. To achieve self-driving autonomy, various sensors are installed onboard the vehicle in order to be able to perceive information from its surroundings. However, since autonomous vehicles’ capabilities rely heavily on sensor readings, various challenges arise in terms of security and privacy. Thus, it is of the essence to design methodologies able to detect anomalies caused by malicious threat actors or sensor malfunctions. This paper proposes an anomaly detection algorithm for autonomous vehicle camera sensors. By utilizing Recurrent Neural Networks in combination with Convolution operations, it is possible to obtain a sequence of images and reconstruct the next frame in real-time. By leveraging image similarity techniques such as Mean Squared Error and Structural Similarity Index, it is possible to compare the ground truth with the predicted image and draw conclusions about whether an anomaly is present. The experiments in real datasets captured from autonomous vehicles within the European-funded nIoVe project highlighted that the proposed framework is able to detect anomalies and malfunctions with high accuracy, clearly indicating the necessity of such algorithms to enhance the security of autonomous vehicles. |
| نوع الوثيقة: |
conference object |
| اللغة: |
unknown |
| Relation: |
https://doi.org/10.1109/MED59994.2023.10185766; oai:zenodo.org:12681326 |
| DOI: |
10.1109/MED59994.2023.10185766 |
| الاتاحة: |
https://doi.org/10.1109/MED59994.2023.10185766 |
| Rights: |
info:eu-repo/semantics/openAccess ; Creative Commons Attribution 4.0 International ; https://creativecommons.org/licenses/by/4.0/legalcode |
| رقم الانضمام: |
edsbas.45C5282A |
| قاعدة البيانات: |
BASE |