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Explainable AI and Evaluation of Algorithms for Autonomous Marine Vehicles
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Explainable AI and Evaluation of Algorithms for Autonomous Marine Vehicles

A special issue of Journal of Marine Science and Engineering (ISSN 2077-1312). This special issue belongs to the section "Ocean Engineering".

Deadline for manuscript submissions: closed (1 March 2022) | Viewed by 14306

Special Issue Editor

Dr. Robin T. Bye

E-Mail Website
Guest Editor
Cyber-Physical Systems Laboratory, Department of ICT and Natural Sciences, NTNU—Norwegian University of Science and Technology, NO-6025 Ålesund, Norway
Interests: cybernetics; AI; neuroengineering; education

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) is an enabling technology for autonomous marine vehicles, including autonomous surface vehicles (ASVs) and autonomous underwater vehicles (AUVs). Algorithms such as fast marching methods, evolutionary algorithms, artificial potential fields, neural networks, reinforcement learning, and many others are becoming increasingly popular for solving problems such as autonomous path planning and collision avoidance. However, there is currently no unified way to evaluate the performance of different algorithms, for example, with regard to safety or risk. In addition, the solutions produced by the algorithms must be understood by a human-in-the loop and from a legal rights and regulatory perspective as well as by other autonomous marine vehicles.

Hence, we invite papers relating to these challenges of algorithms for autonomous marine vehicles, which may include (but are not limited to) one or more of the following aspects:

  • Explainable AI (XAI)
  • Simulated environments and frameworks
  • Scenario generation
  • Human-in-the-loop and human factors
  • Physical field testing
  • Performance metrics and benchmarking
  • Standards, laws, and regulations, including COLREGs
  • Good seamanship
  • Marine traffic control
  • Decision support systems
  • Remote control centers

Papers involving work-in-progress, concepts and ideas, or review papers are also welcome.

Assoc. Prof. Robin T. Bye
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Marine Science and Engineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • autonomous surface vehicle (ASV)
  • autonomous underwater vehicle (AUV)
  • explainable artificial intelligence (XAI)
  • algorithm
  • path planning
  • collision avoidance
  • evaluation
  • safety and risk
  • simulator
  • human-in-the-loop

Published Papers (5 papers)

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Research

21 pages, 4764 KiB  
Article
Evaluation Simulator Platform for Extended Collision Risk of Autonomous Surface Vehicles
by Anete Vagale
J. Mar. Sci. Eng. 2022, 10(5), 705; https://doi.org/10.3390/jmse10050705 - 21 May 2022
Cited by 2 | Viewed by 1829
Abstract
Autonomous surface vehicles need to be at least as safe as conventional vessels, if not safer, when navigating on waters. With a great deal of navigation algorithms for surface vessels out there, the safety of their produced paths is questionable, and, in most [...] Read more.
Autonomous surface vehicles need to be at least as safe as conventional vessels, if not safer, when navigating on waters. With a great deal of navigation algorithms for surface vessels out there, the safety of their produced paths is questionable, and, in most cases, complicated to assess and compare. Hence, this paper proposes a method for extended collision risk assessment for paths generated by autonomous navigation algorithms as follows: (1) static, dynamic, and historic risk factors are calculated; (2) individual risk value is determined using a fuzzy inference system; (3) the extended collision risk assessment (ECRA) score is acquired using a root-mean-square method. Finally, a comparison of the ECRA score of each path determines the path with the lowest risk. The validation results show that the proposed method is able to detect lower/higher risk scenarios and assign an adequate risk value in most cases. Risk reduction for cautious paths varies up to 8.43%, while risk increases for incautious paths—up to 57.98%. The results indicate that the method could be used for navigation algorithm evaluation and comparison with some improvements. This research also reveals several promising future directions and applications of the method. Full article
(This article belongs to the Special Issue Explainable AI and Evaluation of Algorithms for Autonomous Marine Vehicles)
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18 pages, 10522 KiB  
Article
GPU-Accelerated Multi-Objective Optimal Planning in Stochastic Dynamic Environments
by Rohit Chowdhury, Atharva Navsalkar and Deepak Subramani
J. Mar. Sci. Eng. 2022, 10(4), 533; https://doi.org/10.3390/jmse10040533 - 13 Apr 2022
Cited by 5 | Viewed by 1819
Abstract
The importance of autonomous marine vehicles is increasing in a wide range of ocean science and engineering applications. Multi-objective optimization, where trade-offs between multiple conflicting objectives are achieved (such as minimizing expected mission time, energy consumption, and environmental energy harvesting), is crucial for [...] Read more.
The importance of autonomous marine vehicles is increasing in a wide range of ocean science and engineering applications. Multi-objective optimization, where trade-offs between multiple conflicting objectives are achieved (such as minimizing expected mission time, energy consumption, and environmental energy harvesting), is crucial for planning optimal routes in stochastic dynamic ocean environments. We develop a multi-objective path planner in stochastic dynamic flows by further developing and improving our recently developed end-to-end GPU-accelerated single-objective Markov Decision Process path planner. MDPs with scalarized rewards for multiple objectives are formulated and solved in idealized stochastic dynamic ocean environments with dynamic obstacles. Three simulated mission scenarios are completed to elucidate our approach and capabilities: (i) an agent moving from a start to target by minimizing travel time and net-energy consumption when harvesting solar energy in an uncertain flow; (ii) an agent moving from a start to target by minimizing travel time and-energy consumption with uncertainties in obstacle initial positions; (iii) an agent attempting to cross a shipping channel while avoiding multiple fast moving ships in an uncertain flow. Optimal operating curves are computed in a fraction of the time that would be required for existing solvers and algorithms. Crucially, our solution can serve as the benchmark for other approximate AI algorithms such as Reinforcement Learning and help improve explainability of those models. Full article
(This article belongs to the Special Issue Explainable AI and Evaluation of Algorithms for Autonomous Marine Vehicles)
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23 pages, 6853 KiB  
Article
Human-Centered Explainable Artificial Intelligence for Marine Autonomous Surface Vehicles
by Erik Veitch and Ole Andreas Alsos
J. Mar. Sci. Eng. 2021, 9(11), 1227; https://doi.org/10.3390/jmse9111227 - 06 Nov 2021
Cited by 11 | Viewed by 3486
Abstract
Explainable Artificial Intelligence (XAI) for Autonomous Surface Vehicles (ASVs) addresses developers’ needs for model interpretation, understandability, and trust. As ASVs approach wide-scale deployment, these needs are expanded to include end user interactions in real-world contexts. Despite recent successes of technology-centered XAI for enhancing [...] Read more.
Explainable Artificial Intelligence (XAI) for Autonomous Surface Vehicles (ASVs) addresses developers’ needs for model interpretation, understandability, and trust. As ASVs approach wide-scale deployment, these needs are expanded to include end user interactions in real-world contexts. Despite recent successes of technology-centered XAI for enhancing the explainability of AI techniques to expert users, these approaches do not necessarily carry over to non-expert end users. Passengers, other vessels, and remote operators will have XAI needs distinct from those of expert users targeted in a traditional technology-centered approach. We formulate a concept called ‘human-centered XAI’ to address emerging end user interaction needs for ASVs. To structure the concept, we adopt a model-based reasoning method for concept formation consisting of three processes: analogy, visualization, and mental simulation, drawing from examples of recent ASV research at the Norwegian University of Science and Technology (NTNU). The examples show how current research activities point to novel ways of addressing XAI needs for distinct end user interactions and underpin the human-centered XAI approach. Findings show how representations of (1) usability, (2) trust, and (3) safety make up the main processes in human-centered XAI. The contribution is the formation of human-centered XAI to help advance the research community’s efforts to expand the agenda of interpretability, understandability, and trust to include end user ASV interactions. Full article
(This article belongs to the Special Issue Explainable AI and Evaluation of Algorithms for Autonomous Marine Vehicles)
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27 pages, 4632 KiB  
Article
Explaining a Deep Reinforcement Learning Docking Agent Using Linear Model Trees with User Adapted Visualization
by Vilde B. Gjærum, Inga Strümke, Ole Andreas Alsos and Anastasios M. Lekkas
J. Mar. Sci. Eng. 2021, 9(11), 1178; https://doi.org/10.3390/jmse9111178 - 26 Oct 2021
Cited by 11 | Viewed by 2704
Abstract
Deep neural networks (DNNs) can be useful within the marine robotics field, but their utility value is restricted by their black-box nature. Explainable artificial intelligence methods attempt to understand how such black-boxes make their decisions. In this work, linear model trees (LMTs) are [...] Read more.
Deep neural networks (DNNs) can be useful within the marine robotics field, but their utility value is restricted by their black-box nature. Explainable artificial intelligence methods attempt to understand how such black-boxes make their decisions. In this work, linear model trees (LMTs) are used to approximate the DNN controlling an autonomous surface vessel (ASV) in a simulated environment and then run in parallel with the DNN to give explanations in the form of feature attributions in real-time. How well a model can be understood depends not only on the explanation itself, but also on how well it is presented and adapted to the receiver of said explanation. Different end-users may need both different types of explanations, as well as different representations of these. The main contributions of this work are (1) significantly improving both the accuracy and the build time of a greedy approach for building LMTs by introducing ordering of features in the splitting of the tree, (2) giving an overview of the characteristics of the seafarer/operator and the developer as two different end-users of the agent and receiver of the explanations, and (3) suggesting a visualization of the docking agent, the environment, and the feature attributions given by the LMT for when the developer is the end-user of the system, and another visualization for when the seafarer or operator is the end-user, based on their different characteristics. Full article
(This article belongs to the Special Issue Explainable AI and Evaluation of Algorithms for Autonomous Marine Vehicles)
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18 pages, 4742 KiB  
Article
A Universal Simulation Framework of Shipborne Inertial Sensors Based on the Ship Motion Model and Robot Operating System
by Qianfeng Jing, Haichao Wang, Bin Hu, Xiuwen Liu and Yong Yin
J. Mar. Sci. Eng. 2021, 9(8), 900; https://doi.org/10.3390/jmse9080900 - 20 Aug 2021
Cited by 6 | Viewed by 2368
Abstract
A complete virtual test environment is a powerful tool for Autonomous Surface Vessels (ASVs) research, and the simulation of ship motion and shipborne sensors is one of the prerequisites for constructing such an environment. This paper proposed a universal simulation framework of shipborne [...] Read more.
A complete virtual test environment is a powerful tool for Autonomous Surface Vessels (ASVs) research, and the simulation of ship motion and shipborne sensors is one of the prerequisites for constructing such an environment. This paper proposed a universal simulation framework of shipborne inertial sensors. A ship motion model considering environmental disturbances is proposed to simulate the six-degrees-of-freedom motion of ships. The discrete form of the inertial sensor stochastic error model is derived. The inertial measurement data are simulated by adding artificial errors to a simulated motion status. In addition, the ship motion simulation, inertial measurement simulation, and environment simulation nodes are implemented based on the computational graph architecture of the Robot Operating System (ROS). The benefit from the versatility of the ROS messages, the format of simulated inertial measurement is exactly the same as that of real sensors, which provides a research basis for the fusion perception algorithm based on visual–inertial and laser–inertial sensors in the research field of ASVs. Full article
(This article belongs to the Special Issue Explainable AI and Evaluation of Algorithms for Autonomous Marine Vehicles)
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