Quantitative Processing of Situation Awareness for Autonomous Ships Navigation

The first ever attempt at fully autonomous dock-to-dock operation has been tested and demonstrated successfully at the end of 2018. The revolutionary shift is feared to have a negative impact on the safety of navigation and the getting of real-time situation awareness. Especially, the centralized context onboard could be changed to a distributed context. In navigation safety domain, monitoring, control, assessment of dangerous situations, support of operators of decision-making support system should be implemented in real time. In the context of autonomous ships, decision-making processes will play an important role under such ocean autonomy, therefore the same technologies should consist of adequate system intelligence. At the same time, situation awareness is the key element of the decision-making processes. Although there is substantial research on situation awareness measurement techniques, they are not suitable to directly execute quantitative processing for the situation awareness of autonomous ships navigation. Hence, a novel quantitative model of situation awareness is firstly proposed based on the system safety control structure of remotely controlled vessel. The data source is greatly limited, but the main result still indicates that the probability of operator lose adequate situation awareness of the autonomous ship is significantly higher than the conventional ship. Finally, the paper provides a probabilistic theory and model for high-level abstractions of situation awareness to guide future evaluation of the navigation safety of autonomous ships. http://www.transnav.eu the International Journal on Marine Navigation and Safety of Sea Transportation Volume 13

[1]  Feng-Wu Wang,et al.  Collision risk identification of autonomous ships based on the synergy ship domain , 2018, 2018 Chinese Control And Decision Conference (CCDC).

[2]  Jie Zhang,et al.  Finding the Shortest Path in Stochastic Vehicle Routing: A Cardinality Minimization Approach , 2016, IEEE Transactions on Intelligent Transportation Systems.

[3]  Jakub Montewka,et al.  Towards the development of a system-theoretic model for safety assessment of autonomous merchant vessels , 2018, Reliab. Eng. Syst. Saf..

[4]  Regina A. Pomranky,et al.  The role of trust in automation reliance , 2003, Int. J. Hum. Comput. Stud..

[5]  R. M. Taylor,et al.  Situational Awareness Rating Technique (Sart): The Development of a Tool for Aircrew Systems Design , 2017 .

[6]  Danko Nikolić,et al.  SITUATION AWARENESS AS A PREDICTOR OF PERFORMANCE IN EN ROUTE AIR TRAFFIC CONTROLLERS , 1998 .

[7]  Michael D. Matthews,et al.  Assessing Situation Awareness in Field Training Exercises , 2002 .

[8]  G. Klein,et al.  Cognitive Task Analysis of Teams , 2000 .

[9]  Weidong Zhang,et al.  Concise deep reinforcement learning obstacle avoidance for underactuated unmanned marine vessels , 2018, Neurocomputing.

[10]  Neville A. Stanton,et al.  Situation awareness based on eye movements in relation to the task environment , 2018, Cognition, Technology & Work.

[11]  Jakub Montewka,et al.  System-theoretic approach to safety of remotely-controlled merchant vessel , 2018 .

[12]  Martha Grabowski,et al.  Evaluation of wearable immersive augmented reality technology in safety-critical systems , 2018 .

[13]  Mica R. Endsley,et al.  A Survey of Situation Awareness Requirements in Air-to-Air Combat Fighters , 1993 .

[14]  M. R. Houck,et al.  Tools for assessing situational awareness in an operational fighter environment. , 1994, Aviation, space, and environmental medicine.

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

[16]  Varol Akman,et al.  The Use of Situation Theory in Context Modeling , 1997, Comput. Intell..

[17]  Thomas Porathe,et al.  Autonomous Unmanned Merchant Vessel and its Contribution towards the e-Navigation Implementation: The MUNIN Perspective , 2014 .

[18]  Filippo Sanfilippo,et al.  A multi-sensor fusion framework for improving situational awareness in demanding maritime training , 2017, Reliab. Eng. Syst. Saf..

[19]  Jie Zhang,et al.  Multiagent-Based Route Guidance for Increasing the Chance of Arrival on Time , 2016, AAAI.

[20]  Jie Zhang,et al.  Routing Multiple Vehicles Cooperatively: Minimizing Road Network Breakdown Probability , 2017, IEEE Transactions on Emerging Topics in Computational Intelligence.

[21]  J. Barwise,et al.  Scenes and other Situations , 1981 .

[22]  Mikael Wahlström,et al.  Safety and security in autonomous shipping: Challenges for research and development , 2016 .

[23]  Monica Lundh,et al.  Human factor issues during remote ship monitoring tasks: An ecological lesson for system design in a distributed context , 2018, International Journal of Industrial Ergonomics.

[24]  Mica R. Endsley,et al.  Design and Evaluation for Situation Awareness Enhancement , 1988 .

[25]  Jie Zhang,et al.  Maximizing the Probability of Arriving on Time: A Practical Q-Learning Method , 2017, AAAI.

[26]  K. Jon Barwise,et al.  The situation in logic , 1989, CSLI lecture notes series.

[27]  Wei Chen,et al.  An Accurate Solution to the Cardinality-Based Punctuality Problem , 2020, IEEE Intelligent Transportation Systems Magazine.

[28]  Jie Zhang,et al.  A Multiagent-Based Approach for Vehicle Routing by Considering Both Arriving on Time and Total Travel Time , 2017, ACM Trans. Intell. Syst. Technol..

[29]  Keith Devlin,et al.  Situation theory and situation semantics , 2006, Logic and the Modalities in the Twentieth Century.

[30]  Jie Zhang,et al.  Improving the Efficiency of Stochastic Vehicle Routing: A Partial Lagrange Multiplier Method , 2016, IEEE Transactions on Vehicular Technology.