Supporting Intelligent and Trustworthy Maritime Path Planning Decisions

The risk of maritime collisions and groundings has dramatically increased in the past five years despite technological advancements such as GPS-based navigation tools and electronic charts which may add to, instead of reduce, workload. We propose that an automated path planning tool for littoral navigation can reduce workload and improve overall system efficiency, particularly under time pressure. To this end, a Maritime Automated Path Planner (MAPP) was developed, incorporating information requirements developed from a cognitive task analysis, with special emphasis on designing for trust. Human-in-the-loop experimental results showed that MAPP was successful in reducing the time required to generate an optimized path, as well as reducing path lengths. The results also showed that while users gave the tool high acceptance ratings, they rated the MAPP as average for trust, which we propose is the appropriate level of trust for such a system.This work was sponsored by Rite Solutions Inc., Assett Inc., Mikel Inc., and the Office of Naval Research. We would also like to thank Northeast Maritime Institute, the MIT NROTC detachment, the crew of the USS New Hampshire, and the anonymous reviewers whose comments significantly improved the paper

Human-Automation Path Planning Optimization and Decision Support

Path planning is a problem encountered in multiple domains, including unmanned vehicle control, air traffic control, and future exploration missions to the Moon and Mars. Due to the voluminous and complex nature of the data, path planning in such demanding environments requires the use of automated planners. In order to better understand how to support human operators in the task of path planning with computer aids, an experiment was conducted with a prototype path planner under various conditions to assess the effect on operator performance. Participants were asked to create and optimize paths based on increasingly complex path cost functions, using different map visualizations including a novel visualization based on a numerical potential field algorithm. They also planned paths under degraded automation conditions. Participants exhibited two types of analysis strategies, which were global path regeneration and local sensitivity analysis. No main effect due to visualization was detected, but results indicated that the type of optimizing cost function affected performance, as measured by metabolic costs, sun position, path distance and task time. Unexpectedly, participants were able to better optimize more complex cost functions as compared to a simple time-based cost function.We would like to acknowledge the NASA Harriett G. Jenkins Predoctoral Fellowship, the American Association of University Women (AAUW) Dissertation Fellowship, and the Office of Naval Research for sponsoring this research

Applying insights on categorisation, communication, and dynamic decision-making: A case study of a ‘simple’ maritime military decision

A complete understanding of decision-making in military domains requires gathering insights from several fields of study. To make the task tractable, here we consider a specific example of short-term tactical decisions under uncertainty made by the military at sea. Through this lens, we sketch out relevant literature from three psychological tasks each underpinned by decision-making processes: categorisation, communication, and choice. From the literature, we note two general cognitive tendencies that emerge across all three stages: the effect of cognitive load and individual differences. Drawing on these tendencies, we recommend strategies, tools and future research that could improve performance in military domains—but, by extension, would also generalise to other high-stakes contexts. In so doing, we show the extent to which domain general properties of high order cognition are sufficient in explaining behaviours in domain specific contexts

An Experimental Study of the Effect of Transparency on Pilot Trust in the Emergency Landing Planner

This experimental study examined the effects of transparency (operationalized as increasing levels of explanation) on pilot trust of an automated emergency landing planner. A low-fidelity study was conducted where commercial pilots (N12) interacted with simulated recommendations from NASA's Emergency Landing Planner (ELP). These recommendations varied in their associated levels of transparency. Results indicated that trust in the ELP was influenced by the level of transparency within the human-machine interface of the ELP

Saving energy at sea: seafarers’ adoption, appropriation and enactment of technologies supporting energy efficiency

The shipping industry is currently facing a major challenge related to environmental sustainability and energy efficiency. New regulations and ambitious international goals that aim at mitigating carbon-based emissions with 50 %, demands on profitability, along with a growing awareness about the climate change, has prompted the maritime sector to increasingly focus on how to improve energy efficiency and reduce fuel consumption in ship operations. This thesis aims at describing and understanding the challenges of improving energy efficiency seen from the lens of crew members’ work and to investigate the adoption, appropriation and use of particular technologies, purported to support energy efficiency in ship operation. Using an ethnographic approach and drawing on various practice-based concepts and theories such as communities of practice, activity theory and the imbrication of material and social agency, the four papers (I – IV) included in the thesis were based on extensive field studies in two shipping companies and onboard 11 passenger ferries. The empirical studies revealed that the introduction of new technologies and their subsequent incorporation in and change of established skills and practices is a complex social process depending on the knowing and learning of practitioners as well as their activities, meanings, identities and norms as developed and negotiated in specific settings over time. The thesis contributes to our general understanding of the situated process of adoption, appropriation and use of new technologies in the maritime domain and the sociomaterial nature of energy efficiency

Modeling real-time human-automation collaborative scheduling of unmanned vehicles

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2013.This electronic version was submitted and approved by the author's academic department as part of an electronic thesis pilot project. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from department-submitted PDF version of thesis.Includes bibliographical references (p. 325-336).Recent advances in autonomy have enabled a future vision of single operator control of multiple heterogeneous Unmanned Vehicles (UVs). Real-time scheduling for multiple UVs in uncertain environments will require the computational ability of optimization algorithms combined with the judgment and adaptability of human supervisors. Automated Schedulers (AS), while faster and more accurate than humans at complex computation, are notoriously "brittle" in that they can only take into account those quantifiable variables, parameters, objectives, and constraints identified in the design stages that were deemed to be critical. Previous research has shown that when human operators collaborate with AS in real-time operations, inappropriate levels of operator trust, high operator workload, and a lack of goal alignment between the operator and AS can cause lower system performance and costly or deadly errors. Currently, designers trying to address these issues test different system components, training methods, and interaction modalities through costly human-in-the-loop testing. Thus, the objective of this thesis was to develop and validate a computational model of real-time human-automation collaborative scheduling of multiple UVs. First, attributes that are important to consider when modeling real-time human-automation collaborative scheduling were identified, providing a theoretical basis for the model proposed in this thesis. Second, a Collaborative Human-Automation Scheduling (CHAS) model was developed using system dynamics modeling techniques, enabling the model to capture non-linear human behavior and performance patterns, latencies and feedback interactions in the system, and qualitative variables such as human trust in automation. The CHAS model can aid a designer of future UV systems by simulating the impact of changes in system design and operator training on human and system performance. This can reduce the need for time-consuming human-in-the-loop testing that is typically required to evaluate such changes. It can also allow the designer to explore a wider trade space of system changes than is possible through prototyping or experimentation. Through a multi-stage validation process, the CHAS model was tested on three experimental data sets to build confidence in the accuracy and robustness of the model under different conditions. Next, the CHAS model was used to develop recommendations for system design and training changes to improve system performance. These changes were implemented and through an additional set of human subject experiments, the quantitative predictions of the CHAS model were validated. Specifically, test subjects who play computer and video games frequently were found to have a higher propensity to over-trust automation. By priming these gamers to lower their initial trust to a more appropriate level, system performance was improved by 10% as compared to gamers who were primed to have higher trust in the AS. The CHAS model provided accurate quantitative predictions of the impact of priming operator trust on system performance. Finally, the boundary conditions, limitations, and generalizability of the CHAS model for use with other real-time human-automation collaborative scheduling systems were evaluated.by Andrew S. Clare.Ph.D