Real-Time Quality Monitoring and Anomaly Detection for Vision Sensors in Connected and Autonomous Vehicles
The emergence of Connected and Autonomous Vehicles (CAVs) has revolutionized the transportation landscape paving the way for enhanced traffic mobility and innovative and sustainable transportation. Autonomous vehicles rely on sensor data to obtain information of the internal state of the system and...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10858123/ |
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| author | Elena Politi Charalampos Davalas Christos Chronis George Dimitrakopoulos Dimitrios Michail Iraklis Varlamis |
| author_facet | Elena Politi Charalampos Davalas Christos Chronis George Dimitrakopoulos Dimitrios Michail Iraklis Varlamis |
| author_sort | Elena Politi |
| collection | DOAJ |
| description | The emergence of Connected and Autonomous Vehicles (CAVs) has revolutionized the transportation landscape paving the way for enhanced traffic mobility and innovative and sustainable transportation. Autonomous vehicles rely on sensor data to obtain information of the internal state of the system and the impact of the external environment to achieve self-driving autonomy. In that sense, anomalies in sensor readings may reflect faults in sensor components, which in turn can compromise the decision-making functionality of the autonomous system. Video Anomaly Detection (VAD) serves as a pivotal technology in intelligent surveillance systems, enabling the temporal or spatial identification of anomalous events within video frames. This paper proposes a supervised video anomaly assessment framework for autonomous vehicles leveraging Image Quality Assessment (IQA) and Drift Detection techniques. On this basis we adopt a two-stage approach to validate the performance of the proposed methods against a baseline Convolutional Neural Network (CNN) in a controlled low-criticality environment, as well as in more complex real-world scenarios. Our experimental results demonstrate high accuracy and robustness for anomaly detection in comparison to existing supervised techniques, which is crucial for real-time and autonomous operations of CAVs. |
| format | Article |
| id | doaj-art-dc2d5eb3ab024d54be68aa36b570e6a5 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-dc2d5eb3ab024d54be68aa36b570e6a52025-02-12T00:01:34ZengIEEEIEEE Access2169-35362025-01-0113255572556710.1109/ACCESS.2025.353652410858123Real-Time Quality Monitoring and Anomaly Detection for Vision Sensors in Connected and Autonomous VehiclesElena Politi0https://orcid.org/0000-0001-8795-5560Charalampos Davalas1https://orcid.org/0000-0003-4445-4314Christos Chronis2https://orcid.org/0000-0002-2768-7119George Dimitrakopoulos3https://orcid.org/0000-0002-7424-8557Dimitrios Michail4https://orcid.org/0000-0002-5316-6704Iraklis Varlamis5https://orcid.org/0000-0002-0876-8167Department of Informatics and Telematics, Harokopio University of Athens, Athens, GreeceDepartment of Informatics and Telematics, Harokopio University of Athens, Athens, GreeceDepartment of Informatics and Telematics, Harokopio University of Athens, Athens, GreeceDepartment of Informatics and Telematics, Harokopio University of Athens, Athens, GreeceDepartment of Informatics and Telematics, Harokopio University of Athens, Athens, GreeceDepartment of Informatics and Telematics, Harokopio University of Athens, Athens, GreeceThe emergence of Connected and Autonomous Vehicles (CAVs) has revolutionized the transportation landscape paving the way for enhanced traffic mobility and innovative and sustainable transportation. Autonomous vehicles rely on sensor data to obtain information of the internal state of the system and the impact of the external environment to achieve self-driving autonomy. In that sense, anomalies in sensor readings may reflect faults in sensor components, which in turn can compromise the decision-making functionality of the autonomous system. Video Anomaly Detection (VAD) serves as a pivotal technology in intelligent surveillance systems, enabling the temporal or spatial identification of anomalous events within video frames. This paper proposes a supervised video anomaly assessment framework for autonomous vehicles leveraging Image Quality Assessment (IQA) and Drift Detection techniques. On this basis we adopt a two-stage approach to validate the performance of the proposed methods against a baseline Convolutional Neural Network (CNN) in a controlled low-criticality environment, as well as in more complex real-world scenarios. Our experimental results demonstrate high accuracy and robustness for anomaly detection in comparison to existing supervised techniques, which is crucial for real-time and autonomous operations of CAVs.https://ieeexplore.ieee.org/document/10858123/Connected and autonomous vehiclessupervised learningfault detectionvideo anomaly detection |
| spellingShingle | Elena Politi Charalampos Davalas Christos Chronis George Dimitrakopoulos Dimitrios Michail Iraklis Varlamis Real-Time Quality Monitoring and Anomaly Detection for Vision Sensors in Connected and Autonomous Vehicles IEEE Access Connected and autonomous vehicles supervised learning fault detection video anomaly detection |
| title | Real-Time Quality Monitoring and Anomaly Detection for Vision Sensors in Connected and Autonomous Vehicles |
| title_full | Real-Time Quality Monitoring and Anomaly Detection for Vision Sensors in Connected and Autonomous Vehicles |
| title_fullStr | Real-Time Quality Monitoring and Anomaly Detection for Vision Sensors in Connected and Autonomous Vehicles |
| title_full_unstemmed | Real-Time Quality Monitoring and Anomaly Detection for Vision Sensors in Connected and Autonomous Vehicles |
| title_short | Real-Time Quality Monitoring and Anomaly Detection for Vision Sensors in Connected and Autonomous Vehicles |
| title_sort | real time quality monitoring and anomaly detection for vision sensors in connected and autonomous vehicles |
| topic | Connected and autonomous vehicles supervised learning fault detection video anomaly detection |
| url | https://ieeexplore.ieee.org/document/10858123/ |
| work_keys_str_mv | AT elenapoliti realtimequalitymonitoringandanomalydetectionforvisionsensorsinconnectedandautonomousvehicles AT charalamposdavalas realtimequalitymonitoringandanomalydetectionforvisionsensorsinconnectedandautonomousvehicles AT christoschronis realtimequalitymonitoringandanomalydetectionforvisionsensorsinconnectedandautonomousvehicles AT georgedimitrakopoulos realtimequalitymonitoringandanomalydetectionforvisionsensorsinconnectedandautonomousvehicles AT dimitriosmichail realtimequalitymonitoringandanomalydetectionforvisionsensorsinconnectedandautonomousvehicles AT iraklisvarlamis realtimequalitymonitoringandanomalydetectionforvisionsensorsinconnectedandautonomousvehicles |