Human-Automation Collaboration in Complex Multivariate Resource Allocation Decision Support Systems

In resource allocation problems for systems with moving planning horizons and significant uncertainty, typical of supervisory control environments, it is critical that some balance of human-automation collaboration be achieved. These systems typically require leveraging the computational power of automation, as well as the experience and judgment of human decision makers. Human-automation collaboration can occur through degrees of collaboration from automation-centric to human-centric, and such collaboration is inherently distinct from previously-discussed levels of automation. In the context of a command and control mission planning task, we show that across a number of metrics, there is no clear dominant human-automation collaboration scheme for resource allocation problems using three distinct instantiations of human-automation collaboration. Rather, the ultimate selection for the best resource allocation decision support system will depend on a cost-benefit approach that could include mitigation of workload, conformance to intended design characteristics, as well as the need to maximize overall mission performance

Global vs. local decision support for multiple independent UAV schedule management

As unmanned aerial vehicles (UAVs) become increasingly autonomous, time-critical and complex single-operator systems will require advance prediction and mitigation of schedule conflicts. However, actions that mitigate a current schedule conflict may create future schedule problems. Decision support is needed allowing an operator to evaluate different mission schedule management options in real-time. This paper describes two decision support visualisations for single-operator supervisory control of four independent UAVs performing a time-critical targeting mission. A configural display common to both visualisations, called StarVis, graphically depicts current schedule problems, as well as projections of potential local and global schedule problems. Results from an experiment showed that subjects using the locally optimal StarVis implementation had better performance, higher situational awareness, and no significant increase in workload over a more globally optimal implementation of StarVis. This research effort highlights how the same decision support design applied at different abstraction levels can produce different performance results.This research was sponsored by Mitre, Inc

Shared Authority Concerns in Automated Driving Applications

Given the move toward driverless cars, which includes the more short-term goal of driving assistance, what the appropriate shared authority and interaction paradigms should be between human drivers and the automation remains an open question until more principled research and testing has occurred. It is unclear at this time how robust driverless cars are to system failures (including human failures) and operations in degraded sensor environments. Automation onboard such vehicles is inherently brittle and can only account for what it is programmed to consider. Communication between what is technically a very complex system to a human population of extreme variability in driving skills and attention management will be difficult, since the driver will need to be appropriately informed of the state of the system, including limitations, and will need to build appropriate trust in the automation’s capabilities (neither too much or too little). Further complicating this problem is the significant body of research demonstrating that automated systems can lead to boredom, which encourages distraction. This leaves operators unaware of the state of the vehicle (aka, mode confusion) and ill-suited to respond quickly and appropriately in case of a potential accident. Over time, operator skill degradation due to automation use can further reduce the human ability to respond to emergent driving demands, and will likely lead to risk homeostasis even in normal operations. Each of these issues are well-known to the human systems engineering community, but it is unclear that these issues are being considered by driverless car designers or that manufactures are conducting human-in-the-loop tests with representative members of the driving population. Until these tests show that the vehicles account for the aforementioned issues, driverless cars will not be safe for unrestricted access and use on U.S. roadways. Moreover, there are significant socio-technical considerations that do not appear to be a concern in the push to introduce this technology on a wide scale. The utilitarian approach quoted by many in the press, i.e., that driverless cars will eventually kill people but that this should be acceptable due to the likely reduction in overall deaths (which is not yet proven) demonstrates an insensitivity to a deontological perspective that causes many people to be uncomfortable with such a significant shift in responsibility and accountability to computers

The Role of Human-Automation Consensus in Multiple Unmanned Vehicle Scheduling

Objective: This study examined the impact of increasing automation replanning rates on operator performance and workload when supervising a decentralized network of heterogeneous unmanned vehicles. Background: Futuristic unmanned vehicles systems will invert the operator-to-vehicle ratio so that one operator can control multiple dissimilar vehicles connected through a decentralized network. Significant human-automation collaboration will be needed because of automation brittleness, but such collaboration could cause high workload. Method: Three increasing levels of replanning were tested on an existing multiple unmanned vehicle simulation environment that leverages decentralized algorithms for vehicle routing and task allocation in conjunction with human supervision. Results: Rapid replanning can cause high operator workload, ultimately resulting in poorer overall system performance. Poor performance was associated with a lack of operator consensus for when to accept the automation’s suggested prompts for new plan consideration as well as negative attitudes toward unmanned aerial vehicles in general. Participants with video game experience tended to collaborate more with the automation, which resulted in better performance. Conclusion: In decentralized unmanned vehicle networks, operators who ignore the automation’s requests for new plan consideration and impose rapid replans both increase their own workload and reduce the ability of the vehicle network to operate at its maximum capacity. Application: These findings have implications for personnel selection and training for futuristic systems involving human collaboration with decentralized algorithms embedded in networks of autonomous systems.Aurora Flight Sciences Corp.United States. Office of Naval Researc

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

The Impact of Multi-layered Data-blocks on Controller Performance

As a consequence of the push to increase National Airspace System capacity, air traffic control displays will not only have to show the increasing number of aircraft, but also all the associated data such as airspeed and altitude. The representation of aircraft data and associated relational information, often superimposed on a map, leads to cluttered displays, which could negatively affect controller performance, especially as aircraft numbers increase. To investigate these issues further, an experiment was conducted that examined the effect of increasing data-block lines on controller performance in an aircraft vectoring task. Data-block design, the primary factor, varied in the number of lines displayed (2-5). In addition a data-block information priority factor was examined that addressed the frequency of information access across data-block lines. Results demonstrated that while task load, measured as an increasing number of planes under control, negatively influenced reaction times and task accuracy, the number of lines in a data block was not statistically significant. However there was a trend towards reduced performance when data-blocks exceeded more than three lines on a base layer. In addition, the data blocks that contained prioritized information across levels promoted faster reaction times, but at a cost of lower situation awareness. This research demonstrated that the design of data-blocks should consider the balance between reduction in data-block interaction time against the need to allow enough interaction time to build situation awareness.Civil Aerospace Medical Institut

Assessing Operator Strategies for Real-time Replanning of Multiple Unmanned Vehicles

Future unmanned vehicles systems will invert the operator-to-vehicle ratio so that one operator controls a decentralized network of heterogeneous unmanned vehicles. This study examines the impact of allowing an operator to adjust the rate of prompts to view automation-generated plans on system performance and operator workload. Results showed that the majority of operators chose to adjust the replan prompting rate. The initial replan prompting rate had a significant framing effect on the replan prompting rates chosen throughout a scenario. Higher initial replan prompting rates led to significantly lower system performance. Operators successfully self-regulated their task-switching behavior to moderate their workload.This research is funded by the Office of Naval Research (ONR) and Aurora Flight Sciences

Past, Present And Future Implications Of Human Supervisory Control In Space Missions

Achieving the United States’ Vision for future Space Exploration will necessitate far greater collaboration between humans and automated technology than previous space initiatives. However, the development of methodologies to optimize this collaboration currently lags behind development of the technologies themselves, thus potentially decreasing mission safety, efficiency and probability of success. This paper discusses the human supervisory control (HSC) implications for use in space, and outlines several areas of current automated space technology in which the function allocation between humans and machines/automation is sub-optimal or under dispute, including automated spacecraft landings, Mission Control, and wearable extra-vehicular activity computers. Based on these case studies, we show that a more robust HSC research program will be crucial to achieving the Vision for Space Exploration, especially given the limited resources under which it must be accomplished

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 Factors Analysis of Predator B Crash

The 2006 crash of a Predator B in Arizona has prompted a great amount of scrutiny into Unmanned Arial System (UAS) operations. The direct cause of the Predator crash can be tied to an initial failure of the displays and a failed transfer of controls between operators. However, using the Human Factors Analysis and Classification System (HFACS), many latent errors that contributed to the accident were uncovered that were not addressed by the National Transportation Safety Board (NTSB) report. The HFACS approach for this accident examined all issues leading up to the crash and uncovered several organizational influences that were significant contributors to the Predator crash. Through augmenting NTSB efforts with the HFACS method, future UAS incidents can be prevented by addressing all causes, regardless of their distance from the pilot’s seat