Infothek
Neuer Konferenzbeitrag für die CogSIMA
Shang Gao, Zhixin Huang, Ghassan Al-Falouji, Bernhard Sick und Sven Tomforde haben einen Konferenzbeitrag mit dem Titel Towards Cognitive Situational Awareness in Maritime Traffic Using Federated Evidential Learning für die IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA) verfasst.
Und darum geht es:
The increasing complexity of maritime navigation and the shift towards (semi)autonomous systems necessitate enhanced situational awareness (SA) to ensure maritime safety. This transition introduces new requirements for situation mod- elling and SA, particularly in busy waterways. To address these challenges, we present the Federated Evidential Learning for Anomaly Detection of Ship Trajectories (FEAST) framework, which integrates Federated Learning and Evidential Learning to provide a privacy-preserving, collaborative, and uncertainty- aware approach to out-of-distribution (OOD) anomaly detection in maritime traffic. FEAST uses data from the Automatic Identification System from the Kiel region, Germany, which exhibits unique characteristics of dynamic and heterogeneous maritime activity due to its connection with the traffic-dense Kiel Canal. Our extensive evaluations demonstrate that FEAST improves OOD anomaly detection by leveraging epistemic and aleatoric uncertainty estimates, outperforming baseline methods such as Denoise AutoEncoders and Variational AutoEncoders. Consequently, FEAST forms a solution to reliable and inter- pretable maritime traffic anomaly detection, supporting enhanced SA in maritime operations.
Aktuelles
Neuer Konferenzbeitrag für die CogSIMA
Shang Gao, Zhixin Huang, Ghassan Al-Falouji, Bernhard Sick und Sven Tomforde haben einen Konferenzbeitrag mit dem Titel Towards Cognitive Situational Awareness in Maritime Traffic Using Federated Evidential Learning für die IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA) verfasst.
Und darum geht es:
The increasing complexity of maritime navigation and the shift towards (semi)autonomous systems necessitate enhanced situational awareness (SA) to ensure maritime safety. This transition introduces new requirements for situation mod- elling and SA, particularly in busy waterways. To address these challenges, we present the Federated Evidential Learning for Anomaly Detection of Ship Trajectories (FEAST) framework, which integrates Federated Learning and Evidential Learning to provide a privacy-preserving, collaborative, and uncertainty- aware approach to out-of-distribution (OOD) anomaly detection in maritime traffic. FEAST uses data from the Automatic Identification System from the Kiel region, Germany, which exhibits unique characteristics of dynamic and heterogeneous maritime activity due to its connection with the traffic-dense Kiel Canal. Our extensive evaluations demonstrate that FEAST improves OOD anomaly detection by leveraging epistemic and aleatoric uncertainty estimates, outperforming baseline methods such as Denoise AutoEncoders and Variational AutoEncoders. Consequently, FEAST forms a solution to reliable and inter- pretable maritime traffic anomaly detection, supporting enhanced SA in maritime operations.
Termine
Neuer Konferenzbeitrag für die CogSIMA
Shang Gao, Zhixin Huang, Ghassan Al-Falouji, Bernhard Sick und Sven Tomforde haben einen Konferenzbeitrag mit dem Titel Towards Cognitive Situational Awareness in Maritime Traffic Using Federated Evidential Learning für die IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA) verfasst.
Und darum geht es:
The increasing complexity of maritime navigation and the shift towards (semi)autonomous systems necessitate enhanced situational awareness (SA) to ensure maritime safety. This transition introduces new requirements for situation mod- elling and SA, particularly in busy waterways. To address these challenges, we present the Federated Evidential Learning for Anomaly Detection of Ship Trajectories (FEAST) framework, which integrates Federated Learning and Evidential Learning to provide a privacy-preserving, collaborative, and uncertainty- aware approach to out-of-distribution (OOD) anomaly detection in maritime traffic. FEAST uses data from the Automatic Identification System from the Kiel region, Germany, which exhibits unique characteristics of dynamic and heterogeneous maritime activity due to its connection with the traffic-dense Kiel Canal. Our extensive evaluations demonstrate that FEAST improves OOD anomaly detection by leveraging epistemic and aleatoric uncertainty estimates, outperforming baseline methods such as Denoise AutoEncoders and Variational AutoEncoders. Consequently, FEAST forms a solution to reliable and inter- pretable maritime traffic anomaly detection, supporting enhanced SA in maritime operations.