Bayesian Analysis of Behaviors and Interactions for Situation Awareness in Transportation Systems

In large and crowded transportation sites such as ports, parking lots, or busy streets, the unsafe actions of a moving object (such as a pedestrian, a ship, or a car) may compromise the safety and security of the entire infrastructure. It follows that a full comprehension of the situations taking place in the area could significantly decrease the amount of work performed by human administrators and operators in charge of the zone, reducing also the impact of human errors. The idea behind this paper is to use trajectory data to analyze behaviors (atomic actions without any external influence) and interactions (actions inducted by the presence of another entity) of each target in the scene. Probabilistic techniques applied on a topological map of the scene allow tackling the problem in a robust way, handling the uncertainties arising in such environments. The system is tested in a real maritime scenario, where trajectory data of ships and vessels are used to determine normal and abnormal situations occurring in a canal.

[1]  Zoltán Papp.  Situational Awareness in Intelligent Vehicles , 2012 .

[2]  Uwe Glässer,et al.  Anomaly detection in spatiotemporal data in the maritime domain , 2012, 2012 IEEE International Conference on Intelligence and Security Informatics.

[3]  Guangquan Zhang,et al.  The explosion at institute: modeling and analyzing the situation awareness factor. , 2014, Accident; analysis and prevention.

[4]  James A. Anderson,et al.  Neurocomputing: Foundations of Research , 1988 .

[5]  Jan Bulla,et al.  Application of Hidden Markov Models and Hidden Semi-Markov Models to Financial Time Series , 2006 .

[6]  Roozbeh Farahbod,et al.  Engineering Situation Analysis Decision Support Systems , 2011, 2011 European Intelligence and Security Informatics Conference.

[7]  Thomas Martinetz,et al.  Topology representing networks , 1994, Neural Networks.

[8]  Katrien Eloot,et al.  Ship behaviour in shallow and confined water: an overview of hydrodynamic effects through EFD , 2011 .

[9]  Christopher D. Wickens,et al.  Situation Awareness: Review of Mica Endsley's 1995 Articles on Situation Awareness Theory and Measurement , 2008, Hum. Factors.

[10]  J. Beyerer,et al.  Acceptance of automatic situation assessment in surveillance systems , 2012, 2012 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support.

[11]  Akiko Nakata,et al.  Situation awareness in air traffic control : enhanced displays for advanced operations , 2000 .

[12]  Stuart J. Russell,et al.  Dynamic bayesian networks: representation, inference and learning , 2002 .

[13]  Helge J. Ritter,et al.  An instantaneous topological mapping model for correlated stimuli , 1999, IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339).

[14]  Bart De Schutter,et al.  IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS Editor-In-Chief , 2005 .

[15]  Leto Peel,et al.  Maritime anomaly detection using Gaussian Process active learning , 2012, 2012 15th International Conference on Information Fusion.

[16]  Werner Retschitzegger,et al.  BeAware! - Situation awareness, the ontology-driven way , 2010, Data Knowl. Eng..

[17]  Bernd Fritzke,et al.  A Growing Neural Gas Network Learns Topologies , 1994, NIPS.

[18]  C. B. Barrass,et al.  Ship Design and Performance for Masters and Mates , 2004 .

[19]  M. Endsley.  Situation Awareness Misconceptions and Misunderstandings , 2015 .

[20]  Xavier Lerouvreur,et al.  Unsupervised extraction of knowledge from S-AIS data for maritime situational awareness , 2013, Proceedings of the 16th International Conference on Information Fusion.

[21]  Francesco Palmieri,et al.  Abnormal vessel behavior detection in port areas based on Dynamic Bayesian Networks , 2014, 17th International Conference on Information Fusion (FUSION).

[22]  Carlo S. Regazzoni,et al.  Interaction Analysis with a Bayesian Trajectory Model , 2010, IEEE Intelligent Systems.

[23]  Ryan Fries,et al.  Transportation Infrastructure Security Utilizing Intelligent Transportation Systems , 2008 .

[24]  Lauro Snidaro,et al.  Fusing uncertain knowledge and evidence for maritime situational awareness via Markov Logic Networks , 2015, Inf. Fusion.

[25]  Marina L. Gavrilova,et al.  Generalized Voronoi Diagram: A Geometry-Based Approach to Computational Intelligence , 2008, Generalized Voronoi Diagram.

[26]  Mohan M. Trivedi,et al.  A Survey of Vision-Based Trajectory Learning and Analysis for Surveillance , 2008, IEEE Transactions on Circuits and Systems for Video Technology.

[27]  Francesco Palmieri,et al.  Online Bayesian learning and classification of ship-to-ship interactions for port safety , 2014, 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS).

[28]  Mica R. Endsley,et al.  Toward a Theory of Situation Awareness in Dynamic Systems , 1995, Hum. Factors.

[29]  Mica R. Endsley,et al.  Designing for Situation Awareness in Complex System , 2001 .

[30]  Carlo S. Regazzoni,et al.  A bio-inspired system model for interactive surveillance applications , 2011, J. Ambient Intell. Smart Environ..

[31]  Mica R. Endsley,et al.  Measurement of Situation Awareness in Dynamic Systems , 1995, Hum. Factors.

[32]  Andrea F. Cattoni,et al.  Interaction Modeling and Prediction in Smart Spaces: A Bio-Inspired Approach Based on Autobiographical Memory , 2010, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[33]  Jie Lu,et al.  An intelligent situation awareness support system for safety-critical environments , 2014, Decis. Support Syst..

[34]  David Barber,et al.  Bayesian reasoning and machine learning , 2012 .