Supporting intelligent and trustworthy maritime path planning decisions

Cummings, Mary L.; Buchin, Mariela E; Carrigan, Geoffrey P; Donmez, Birsen

doi:10.1016/j.ijhcs.2010.05.002

Cited by 28 publications

(21 citation statements)

References 21 publications

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“…Therefore, the effect of lower trust values reported by the drivers aided by the visualization of the car's (un)certainty can be easily explained if we consider that those that could see the car's (un)certainty of its capabilities could adjust their trust levels better than the group who was not provided with this information. We believe that if we show this type of meta-information we support trust calibration, and, based on our experiences, we agree with Cummings et al's [29] recommendation, i.e., designers of intelligent systems should seek some median level of acceptable trust, and that high and low ratings are both equally problematic.…”

Section: Performancesupporting

confidence: 79%

“…A possible explanation for interpreting our results can be found in [15] and [29]. Dzindolet et al [15], where the role of trust in automation reliance is studied, suggest that participants initially considered automated decision aids trustworthy and reliable, but, after observing the automated aid make errors, participants distrusted even reliable aids, unless an explanation was provided regarding why the aid might err.…”

Section: Performancementioning

confidence: 72%

“…A known negative consequence of too much trust in an automated generated solution is automation bias, where humans do not seek disconfirming evidence and fully trust the system's recommendations (this effect has been seen in, for example, C2 systems [29]). Over-trust can lead to serious problems in many settings, and we should not necessarily desire the highest trust rating in an automated system, especially when the underlying automation that drives the system is flawed [29].…”

Section: Performancementioning

confidence: 99%

“…Over-trust can lead to serious problems in many settings, and we should not necessarily desire the highest trust rating in an automated system, especially when the underlying automation that drives the system is flawed [29]. Therefore, the effect of lower trust values reported by the drivers aided by the visualization of the car's (un)certainty can be easily explained if we consider that those that could see the car's (un)certainty of its capabilities could adjust their trust levels better than the group who was not provided with this information.…”

Section: Performancementioning

confidence: 99%

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Influence of meta-information on decision-making: Lessons learned from four case studies

Riveiro

Helldin

Falkman

2014

2014 IEEE International Inter-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA

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This paper discusses the results of four empirical evaluations that assess the effects that visualizing system metainformation have on decision-making, particularly on confidence, trust, workload, time and performance. These four case studies correspond to the analysis of (1) the effects that visualizing uncertainty associated with sensor values (position, speed, altitude, etc. and track quality) have on decision-making on a ground to air defense scenario; (2) the effects that the visualization of the car's certainty on its own capability of driving autonomously have on drivers' trust and performance; (3) the influence that the visualization of various qualifiers associated with the proposals given by the support system has on air traffic operators carrying out identification tasks and (4) the effects that the presentation of different abstraction levels of information have on classification tasks carried out by fighter pilots. We summarize the results of these four case studies and discuss lessons learned for the design of future computerized support systems regarding the visualization of meta-information.

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Section: Performancesupporting

confidence: 79%

Section: Performancementioning

confidence: 72%

Section: Performancementioning

confidence: 99%

Section: Performancementioning

confidence: 99%

See 2 more Smart Citations

Influence of meta-information on decision-making: Lessons learned from four case studies

Riveiro

Helldin

Falkman

2014

2014 IEEE International Inter-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA

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show abstract

“…Furthermore, optimization and decision-making theories have rapidly developed in recent years [2,3], enabling the computation of better vessel trajectories in multivessel environments. These include artificial intelligence algorithms such as genetic algorithms (GA) (see [4,5]), ant colony algorithms (ACA) (see [6,7]), fuzzy decision methods (see [8][9][10]), and various other approaches [11][12][13][14][15]. Smierzchalski proposed an evolutionary algorithm to model a ship's trajectory in collision situations (see [16,17]).…”

Section: Introductionmentioning

confidence: 99%

An Improved Differential Evolution Algorithm for Maritime Collision Avoidance Route Planning

Zhao

Feng

et al. 2014

Abstract and Applied Analysis

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High accuracy navigation and surveillance systems are pivotal to ensure efficient ship route planning and marine safety. Based on existing ship navigation and maritime collision prevention rules, an improved approach for collision avoidance route planning using a differential evolution algorithm was developed. Simulation results show that the algorithm is capable of significantly enhancing the optimized route over current methods. It has the potential to be used as a tool to generate optimal vessel routing in the presence of conflicts.

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Fuzzy logic system interference in ship accidents

Psarros

2018

Hum Ftrs & Erg Mfg Svc

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Poor lookout, lack of situation awareness, and delayed response have been continuously recognized as contributory causes of navigation accidents (i.e., collisions and groundings). It is claimed that such negative behavioral characteristics can be corrected when the navigation system is equipped with the automation capability to intervene upon their detection. Building on the results of my previous work, this study sets the starting line in exploring this technological innovation under a conceptual framework, where it is attempted to employ simple, quantifiable, and measurable ways to assess the deck officer's (navigator's) complex aspect of mental task reliability. To deal with this challenge, the navigator's behavior when performing navigation tasks is described by the Markov method and the uncertainty, ambiguity, and weakness in perceiving accurately the environment and traffic conditions is determined by fuzzy logic. The proposed concept is demonstrated through its practical application in collision and grounding accidents, where it is argued that further research is required for its validation. K E Y W O R D Sautomation, cognitive/mental performance reliability, fuzzy logic, human factors, maritime accidents, Markov method

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Supporting intelligent and trustworthy maritime path planning decisions

Cited by 28 publications

References 21 publications

Influence of meta-information on decision-making: Lessons learned from four case studies

Influence of meta-information on decision-making: Lessons learned from four case studies

An Improved Differential Evolution Algorithm for Maritime Collision Avoidance Route Planning

Fuzzy logic system interference in ship accidents

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