Automation in human-machine networks: how increasing machine agency affects human agency

© 2018, Springer International Publishing AG. Efficient human-machine networks require productive interaction between human and machine actors. In this study, we address how a strengthening of machine agency, for example through increasing levels of automation, affect the human actors of the networks. Findings from case studies within air traffic management, emergency management, and crowd evacuation are presented, shedding light on how automation may strengthen the agency of human actors in the network through responsibility sharing and task allocation, and serve as a needed prerequisite of innovation and change

Human-centered challenges and contributions for the implementation of automated driving

Automated driving is expected to increase safety and efficiency of road transport. With regard to the implementation of automated driving, we observed that those aspects which need to be further developed especially relate to human capabilities. Based on this observation and the understanding that automation will most likely be applied in terms of partially automated driving, we distinguished 2 major challenges for the implementation of partially automated driving: (1) Defining appropriate levels of automation, and; (2) Developing appropriate transitions between manual control and automation. The Assisted Driver Model has provided a framework for the first challenge, because this model recommends levels of automation dependent on traffic situations. To conclude, this research also provided brief directions on the second challenge, i.e. solutions how to accommodate drivers with partially automatio

Situational awareness and safety

This paper considers the applicability of situation awareness concepts to safety in the control of complex systems. Much of the research to date has been conducted in aviation, which has obvious safety implications. It is argued that the concepts could be extended to other safety critical domains. The paper presents three theories of situational awareness: the three-level model, the interactive sub-systems approach, and the perceptual cycle. The difference between these theories is the extent to which they emphasise process or product as indicative of situational awareness. Some data from other studies are discussed to consider the negative effects of losing situational awareness, as this has serious safety implications. Finally, the application of situational awareness to system design, and training are presented

Bioclimatic and Soil Moisture Monitoring Across Elevation in a Mountain Watershed: Opportunities for Research and Resource Management

Soil moisture data are critical to understanding biophysical and societal impacts of climate change. However, soil moisture data availability is limited due to sparse in situ monitoring, particularly in mountain regions. Here we present methods, specifications, and initial results from the interactive Roaring Fork Observation Network (iRON), a soil, weather, and ecological monitoring system in the Southern Rocky Mountains of Colorado. Initiated in 2012, the network is currently composed of nine stations, distributed in elevation from 1,890 to 3,680 m, that continually collect and transmit measurements of soil moisture at three depths (5, 20, and 50 cm), soil temperature (20 cm), and meteorological conditions. Time‐lapse cameras for phenological observations, snow depth sensors, and periodic co‐located vegetation surveys complement selected stations. iRON was conceived and designed with the joint purpose of supporting bioclimatic research and resource management objectives in a snow‐dominated watershed. In the short term, iRON data can be applied to assessing the impact of temperature and precipitation on seasonal soil moisture conditions and trends. As more data are collected over time, iRON will help improve understanding of climate‐driven changes to soil, vegetation, and hydrologic conditions. In presenting this network and its initial data, we hope that the network’s elevational gradient will contribute to bioclimatic mountain research, while active collaboration with partners in resource management may provide a model for science‐practice interaction in support of long‐term monitoring.Plain Language SummaryAs climate change drives shifts in temperature and precipitation, researchers and resource managers can benefit from improved monitoring of soil moisture. Understanding the relationship between soil moisture and other system components is crucial to improving water availability projections and understanding ecosystem responses to climate change. Despite their significance, in‐ground soil‐moisture measurements are often not available across multiple elevations within a single watershed. This paper presents a network in the Southern Rocky Mountains intended to help address this data gap and compliment data from other networks. The interactive Roaring Fork Observation Network consists of nine locations across an 1,800‐m change in elevation. Each station measures soil moisture at three depths, soil temperature, air temperature, humidity, and precipitation. Some stations are equipped with cameras or snow depth gauges, and for eight sites vegetation surveys are conducted. The data are available through a simple data portal. The network was established with local resource manager support, and one of its guiding purposes is to support management and restoration planning efforts. Because of the network’s ongoing monitoring across multiple elevations and habitats, interactive Roaring Fork Observation Network will provide researchers and resource managers with access to valuable information about changes in soil conditions in a changing climate.Key PointsSoil moisture is key to understanding and predicting change in hydrology and ecology amid climate variability and changeIn situ soil moisture and weather monitoring data are now available across an 1,800‐m elevation span in a mountain watershedThe network is supported and guided by resource managers and supports both research and resource management goalsPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149210/1/wrcr23834_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149210/2/wrcr23834.pd

Rapid response tools and datasets for post-fire modeling: linking Earth Observations and process-based hydrological models to support post-fire remediation

Preparation is key to utilizing Earth Observations and process-based models to support post-wildfire mitigation. Post-fire flooding and erosion can pose a serious threat to life, property and municipal water supplies. Increased runoff and sediment delivery due to the loss of surface cover and fire-induced changes in soil properties are of great concern. Remediation plans and treatments must be developed and implemented before the first major storms in order to be effective. One of the primary sources of information for making remediation decisions is a soil burn severity map derived from Earth Observation data (typically Landsat) that reflects fire induced changes in vegetation and soil properties. Slope, soils, land cover and climate are also important parameters that need to be considered. Spatially-explicit process-based models can account for these parameters, but they are currently under-utilized relative to simpler, lumped models because they are difficult to set up and require spatially-explicit inputs (digital elevation models, soils, and land cover). Our goal is to make process-based models more accessible by preparing spatial inputs before a fire, so that datasets can be rapidly combined with soil burn severity maps and formatted for model use. We are building an online database (http://geodjango.mtri.org/geowepp /) for the continental United States that will allow users to upload soil burn severity maps. The soil burn severity map is combined with land cover and soil datasets to generate the spatial model inputs needed for hydrological modeling of burn scars. Datasets will be created to support hydrological models, post-fire debris flow models and a dry ravel model. Our overall vision for this project is that advanced GIS surface erosion and mass failure prediction tools will be readily available for post-fire analysis using spatial information from a single online site

Visual Analytics for Network Security and Critical Infrastructures

A comprehensive analysis of cyber attacks is important for better understanding of their nature and their origin. Providing a sufficient insight into such a vast amount of diverse (and sometimes seemingly unrelated) data is a task that is suitable neither for humans nor for fully automated algorithms alone. Not only a combination of the two approaches but also a continuous reasoning process that is capable of generating a sufficient knowledge base is indispensable for a better understanding of the events. Our research is focused on designing new exploratory methods and interactive visualizations in the context of network security. The knowledge generation loop is important for its ability to help analysts to refine the nature of the processes that continuously occur and to offer them a better insight into the network security related events. In this paper, we formulate the research questions that relate to the proposed solution

A Human Situation Awareness Support System to Avoid Technological Disasters

In many complex technological systems, accidents have primarily been attributed to human error. In the majority of these accidents the human operators were striving against significant challenges. They have to face data overload, the challenge of working with a complex system and the stressful task of understanding what is going on in the situation. Therefore, to design and implement complex technological systems where the information flow is quite high, and poor decisions may lead to serious consequences, Situation Awareness (SA) should be appropriately considered. A level 1 SA is highly supported in these systems through the various heterogeneous sensors and signal-processing methods but, for levels 2 and 3 there is still a need for concepts and methods. This work develops a system called the Human Situation Awareness Support System (HSASS) that supports the safety operators in an ever increasing amount of available risky status and alert information. The proposed system includes a new dynamic situation assessment method based on risk, which has the ability to support the operators understanding of the current state of the system, predict the near future, and suggest appropriate actions. The proposed system does not control the course of action and allows the human to act at his/her discretion in specific contexts