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Thill, S., Riveiro, M., Lagerstedt, E., Lebram, M., Hemeren, P., Habibovic, A. & Klingegård, M. (2018). Driver adherence to recommendations from support systems improves if the systems explain why they are given: A simulator study. Transportation Research Part F: Traffic Psychology and Behaviour, 56, 420-435
Open this publication in new window or tab >>Driver adherence to recommendations from support systems improves if the systems explain why they are given: A simulator study
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2018 (English)In: Transportation Research Part F: Traffic Psychology and Behaviour, ISSN 1369-8478, E-ISSN 1873-5517, Vol. 56, p. 420-435Article in journal (Refereed) Published
Abstract [en]

This paper presents a large-scale simulator study on driver adherence to recommendationsgiven by driver support systems, specifically eco-driving support and navigation support.123 participants took part in this study, and drove a vehicle simulator through a pre-defined environment for a duration of approximately 10 min. Depending on the experi-mental condition, participants were either given no eco-driving recommendations, or asystem whose provided support was either basic (recommendations were given in theform of an icon displayed in a manner that simulates a heads-up display) or informative(the system additionally displayed a line of text justifying its recommendations). A naviga-tion system that likewise provided either basic or informative support, depending on thecondition, was also provided.

Effects are measured in terms of estimated simulated fuel savings as well as engine brak-ing/coasting behaviour and gear change efficiency. Results indicate improvements in allvariables. In particular, participants who had the support of an eco-driving system spenta significantly higher proportion of the time coasting. Participants also changed gears atlower engine RPM when using an eco-driving support system, and significantly more sowhen the system provided justifications. Overall, the results support the notion that pro-viding reasons why a support system puts forward a certain recommendation improvesadherence to it over mere presentation of the recommendation.

Finally, results indicate that participants’ driving style was less eco-friendly if the navi-gation system provided justifications but the eco-system did not. This may be due to par-ticipants considering the two systems as one whole rather than separate entities withindividual merits. This has implications for how to design and evaluate a given driver sup-port system since its effectiveness may depend on the performance of other systems in thevehicle.

Keywords
Driver behaviour, System awareness, Eco-friendly behaviour, Driver recommendation systems
National Category
Psychology Human Computer Interaction Information Systems
Research subject
Interaction Lab (ILAB); Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science; INF302 Autonomous Intelligent Systems
Identifiers
urn:nbn:se:hj:diva-43234 (URN)10.1016/j.trf.2018.05.009 (DOI)000437997700037 ()2-s2.0-85048505654 (Scopus ID)0;0;miljJAIL (Local ID)0;0;miljJAIL (Archive number)0;0;miljJAIL (OAI)
Projects
TIEB
Funder
Swedish Energy Agency
Available from: 2018-06-04 Created: 2019-03-05 Last updated: 2019-08-23Bibliographically approved
Riveiro, M., Lebram, M. & Elmer, M. (2017). Anomaly Detection for Road Traffic: A Visual Analytics Framework. IEEE transactions on intelligent transportation systems (Print), 18(8), 2260-2270, Article ID 7887700.
Open this publication in new window or tab >>Anomaly Detection for Road Traffic: A Visual Analytics Framework
2017 (English)In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 18, no 8, p. 2260-2270, article id 7887700Article in journal (Refereed) Published
Abstract [en]

The analysis of large amounts of multidimensional road traffic data for anomaly detection is a complex task. Visual analytics can bridge the gap between computational and human approaches to detecting anomalous behavior in road traffic, making the data analysis process more transparent. In this paper, we present a visual analytics framework that provides support for: 1) the exploration of multidimensional road traffic data; 2) the analysis of normal behavioral models built from data; 3) the detection of anomalous events; and 4) the explanation of anomalous events. We illustrate the use of this framework with examples from a large database of real road traffic data collected from several areas in Europe. Finally, we report on feedback provided by expert analysts from Volvo Group Trucks Technology, regarding its design and usability.

Keywords
Anomaly detection, visual analytics, normal traffic model, intelligent transport systems
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL); Interaction Lab (ILAB); INF301 Data Science; INF302 Autonomous Intelligent Systems
Identifiers
urn:nbn:se:hj:diva-43242 (URN)10.1109/TITS.2017.2675710 (DOI)000407347300022 ()2-s2.0-85017131904 (Scopus ID)0;0;miljJAIL (Local ID)0;0;miljJAIL (Archive number)0;0;miljJAIL (OAI)
Funder
Knowledge Foundation, 20140294
Available from: 2017-09-14 Created: 2019-03-05 Last updated: 2019-08-23Bibliographically approved
Riveiro, M., Gustavsson, P. M., Lebram, M., Bengstsson, M., Blomqvist, P. & Wallinius, M. (2016). Enhanced Training through Interactive Visualization of Training Objectives and Models. In: Proceedings of the STO-MP-MSG-143, Ready for the Predictable, Prepared for the Unexpected: M&S for Collective Defence in Hybrid Environments and Hybrid Conflicts. Paper presented at 2016 NATO Modelling & Simulation Group (NMSG) Symposium, Bucharest, Romania, October 20-21, 2016. NATO Science & Technology Organization (STO)
Open this publication in new window or tab >>Enhanced Training through Interactive Visualization of Training Objectives and Models
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2016 (English)In: Proceedings of the STO-MP-MSG-143, Ready for the Predictable, Prepared for the Unexpected: M&S for Collective Defence in Hybrid Environments and Hybrid Conflicts, NATO Science & Technology Organization (STO) , 2016Conference paper, Published paper (Refereed)
Abstract [en]

Military forces operate in complex and dynamic environments [1] where bad decisions might have fatal consequences. A key ability of the commander, team and individual warfighter is to quickly adapt to novel situations. Live, Virtual and Constructive training environments all provide elements of best practices for this type of training. However, many of the virtual training are designed without thorough consideration of the effectiveness and efficiency of embedded instructional strategies [2], and without considering the cognitive capabilities and limitations of trainees. As highlighted recently by Stacy and Freeman [3], large military training exercises require a significant commitment of resources, and to net a return on that investment, training scenarios for these events should systematically address well-specified training objectives, even if they often, do not.

In order to overcome these shortcomings with both Live and Virtual training systems and following our previous work [4,5,6], this paper presents a design solution for a proof-of-concept prototype that visualizes and manages training objectives and performance measures, at individual and collective levels. To illustrate its functionality we use real-world data from Live training exercises. Finally, this paper discusses how to learn from previous training experiences using data mining methods in order to build training models to provide instructional personalized feedback to trainees.

Place, publisher, year, edition, pages
NATO Science & Technology Organization (STO), 2016
National Category
Computer Sciences
Research subject
Technology; Skövde Artificial Intelligence Lab (SAIL); Interaction Lab (ILAB)
Identifiers
urn:nbn:se:hj:diva-43251 (URN)0;0;miljJAIL (Local ID)978-92-837-2060-7 (ISBN)0;0;miljJAIL (Archive number)0;0;miljJAIL (OAI)
Conference
2016 NATO Modelling & Simulation Group (NMSG) Symposium, Bucharest, Romania, October 20-21, 2016
Projects
NOVA 20140294 (Knowledge Foundation)
Funder
Knowledge Foundation, 20140294
Available from: 2019-03-05 Created: 2019-03-05 Last updated: 2019-08-23Bibliographically approved
Riveiro, M., Lebram, M., Andersson, C. X., Sartipy, P. & Synnergren, J. (2016). Interactive visualization of large-scale gene expression data. In: Ebad Banissi, Mark W. McK. Bannatyne, Fatma Bouali, Remo Burkhard, John Counsell, Urska Cvek, Martin J. Eppler, Georges Grinstein, Wei Dong Huang, Sebastian Kernbach, Chun-Cheng Lin, Feng Lin, Francis T. Marchese, Chi Man Pun, Muhammad Sarfraz, Marjan Trutschl, Anna Ursyn, Gilles Venturini, Theodor G. Wyeld, and Jian J. Zhang (Ed.), Information Visualisation: Computer Graphics, Imaging and Visualisation. Paper presented at 20th International Conference Information Visualisation, 19-22 July 2016, Lisbon, Portugal (pp. 348-354). IEEE Computer Society
Open this publication in new window or tab >>Interactive visualization of large-scale gene expression data
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2016 (English)In: Information Visualisation: Computer Graphics, Imaging and Visualisation / [ed] Ebad Banissi, Mark W. McK. Bannatyne, Fatma Bouali, Remo Burkhard, John Counsell, Urska Cvek, Martin J. Eppler, Georges Grinstein, Wei Dong Huang, Sebastian Kernbach, Chun-Cheng Lin, Feng Lin, Francis T. Marchese, Chi Man Pun, Muhammad Sarfraz, Marjan Trutschl, Anna Ursyn, Gilles Venturini, Theodor G. Wyeld, and Jian J. Zhang, IEEE Computer Society , 2016, p. 348-354Conference paper, Published paper (Refereed)
Abstract [en]

In this article, we present an interactive prototype that aids the interpretation of large-scale gene expression data, showing how visualization techniques can be applied to support knowledge extraction from large datasets. The developed prototype was evaluated on a dataset of human embryonic stem cell-derived cardiomyocytes. The visualization approach presented here supports the analyst in finding genes with high similarity or dissimilarity across different experimental groups. By using an external overview in combination with filter windows, and various color scales for showing the degree of similarity, our interactive visual prototype is able to intuitively guide the exploration processes over the large amount of gene expression data.

Place, publisher, year, edition, pages
IEEE Computer Society, 2016
Series
Proceedings [IEEE], E-ISSN 2375-0138
Keywords
decision-making, gene expression data, similarity, visual analytics
National Category
Computer Sciences
Research subject
Technology; Skövde Artificial Intelligence Lab (SAIL); Interaction Lab (ILAB); Bioinformatics
Identifiers
urn:nbn:se:hj:diva-43253 (URN)10.1109/IV.2016.58 (DOI)000389494200057 ()2-s2.0-84989862491 (Scopus ID)0;0;miljJAIL (Local ID)978-1-4673-8942-6 (ISBN)978-1-4673-8943-3 (ISBN)0;0;miljJAIL (Archive number)0;0;miljJAIL (OAI)
Conference
20th International Conference Information Visualisation, 19-22 July 2016, Lisbon, Portugal
Projects
NOVA and BISON
Funder
Knowledge Foundation, 20140294
Available from: 2016-09-24 Created: 2019-03-05 Last updated: 2019-08-23Bibliographically approved
Riveiro, M., Helldin, T., Falkman, G. & Lebram, M. (2014). Effects of visualizing uncertainty on decision-making in a target identification scenario. Computers & graphics, 41(1), 84-98
Open this publication in new window or tab >>Effects of visualizing uncertainty on decision-making in a target identification scenario
2014 (English)In: Computers & graphics, ISSN 0097-8493, E-ISSN 1873-7684, Vol. 41, no 1, p. 84-98Article in journal (Refereed) Published
Abstract [en]

This paper presents an empirical study that addresses the effects the visualization of uncertainty has on decision-making. We focus our investigations on an area where uncertainty plays an important role and the decision time is limited. For that, we selected an air defense scenario, where expert operators have a few minutes to make a well-informed decision based on uncertain sensor data regarding the identity of an object and where the consequences of a late or wrong decision are severe. An approach for uncertainty visualization is proposed and tested using a prototype that supports the interactive analysis of multivariate spatio-temporal sensor data. The uncertainty visualization embeds the accuracy of the sensor data values using the thickness of the lines in the graphical representation of the sensor values. Semi-transparent filled circles represent the uncertain position, while a track quality value between 0 and 1 accounts for the quality of the estimated track for each target. Twenty-two experienced air traffic operators were divided into two groups (with and without uncertainty visualization) and carried out identification and prioritization tasks using the prototype. The results show that the group aided by visualizations of uncertainty needed significantly fewer attempts to make a final identification, and a significant difference between the groups when considering the identities and priorities assigned was observed (participants with uncertainty visualization selected higher priority values and more hostile and suspect identities). These results may show that experts put themselves in the ``worst-case scenario" in the presence of uncertainty when safety is an issue. Additionally, the presentation of uncertainty neither increased the participants' expressed workload, nor the time needed to make a classification. However, the inclusion of the uncertainty information did not have a significant effect on the performance (true positives, false negatives and false positives) or the participants' expressed confidence in their decisions.

Place, publisher, year, edition, pages
Elsevier, 2014
Keywords
Uncertainty visualization, Decision-making, Confidence, Performance, Workload, Target identification
National Category
Human Computer Interaction
Research subject
Technology; Skövde Artificial Intelligence Lab (SAIL); Interaction Lab (ILAB)
Identifiers
urn:nbn:se:hj:diva-43265 (URN)10.1016/j.cag.2014.02.006 (DOI)000336349700008 ()2-s2.0-84897553740 (Scopus ID)0;0;miljJAIL (Local ID)0;0;miljJAIL (Archive number)0;0;miljJAIL (OAI)
Available from: 2014-05-02 Created: 2019-03-05 Last updated: 2019-08-23Bibliographically approved
Riveiro, M., Lebram, M. & Warston, H. (2014). On visualizing threat evaluation configuration processes: A design proposal. In: 17th International Conference on Information Fusion (FUSION), 2014: . Paper presented at 17th International Conference on Information Fusion, FUSION 2014, Salamanca, Spain 7 July 2014 through 10 July 2014 (pp. 1-8). IEEE conference proceedings
Open this publication in new window or tab >>On visualizing threat evaluation configuration processes: A design proposal
2014 (English)In: 17th International Conference on Information Fusion (FUSION), 2014, IEEE conference proceedings , 2014, p. 1-8Conference paper, Published paper (Refereed)
Abstract [en]

Threat evaluation is concerned with estimating the intent, capability and opportunity of detected objects in relation to our own assets in an area of interest. To infer whether a target is threatening and to which degree is far from a trivial task. Expert operators have normally to their aid different support systems that analyze the incoming data and provide recommendations for actions. Since the ultimate responsibility lies in the operators, it is crucial that they trust and know how to configure and use these systems, as well as have a good understanding of their inner workings, strengths and limitations. To limit the negative effects of inadequate cooperation between the operators and their support systems, this paper presents a design proposal that aims at making the threat evaluationprocess more transparent. We focus on the initialization, configuration and preparation phases of thethreat evaluation process, supporting the user in the analysis of the behavior of the system considering the relevant parameters involved in the threat estimations. For doing so, we follow a known design process model and we implement our suggestions in a proof-of-concept prototype that we evaluate with military expert system designers.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2014
National Category
Engineering and Technology
Research subject
Technology; Skövde Artificial Intelligence Lab (SAIL); Interaction Lab (ILAB)
Identifiers
urn:nbn:se:hj:diva-43268 (URN)000363896100183 ()2-s2.0-84910660640 (Scopus ID)0;0;miljJAIL (Local ID)9788490123553 (ISBN)0;0;miljJAIL (Archive number)0;0;miljJAIL (OAI)
Conference
17th International Conference on Information Fusion, FUSION 2014, Salamanca, Spain 7 July 2014 through 10 July 2014
Funder
Knowledge Foundation
Note

Article number 6916152

Available from: 2014-12-15 Created: 2019-03-05 Last updated: 2019-08-23Bibliographically approved
Riveiro, M., Helldin, T., Lebram, M. & Falkman, G. (2013). Towards future threat evaluation systems: user study, proposal and precepts for design. In: Proceedings of the 16th International Conference on Information Fusion, FUSION 2013: . Paper presented at 16th International Conference on Information Fusion (FUSION), 2013, 9-12 July 2013, Istanbul, Turkey (pp. 1863-1870). IEEE Press
Open this publication in new window or tab >>Towards future threat evaluation systems: user study, proposal and precepts for design
2013 (English)In: Proceedings of the 16th International Conference on Information Fusion, FUSION 2013, IEEE Press , 2013, p. 1863-1870Conference paper, Published paper (Refereed)
Abstract [en]

In the defense domain, to estimate if a targetis threatening and to which degree is a complex task, thatis typically carried out by human operators due to the highrisks and uncertainties associated. To their aid, different supportsystems have been implemented to analyze the data and providerecommendations for actions. Since the ultimate responsibilitylies in human operators, it is of utmost importance that theytrust and know how to use these systems, as well as have anunderstanding of their inner workings, strengths and limitations.This paper presents, first, a formative user study to char-acterize how air traffic operators carry out threat evaluationrelated tasks. Grounded in these findings and in guidelinesfound in the literature, we present a transparent and highlyinteractive prototype that aims at reducing operator’s cognitiveload and support threat assessment activities. The literaturereview provided on design guidelines, the outcomes of the userstudy, the design of the prototype as well as the results of aninitial evaluation can provide guidance for both researchers andprospective developers of future threat evaluation systems.

Place, publisher, year, edition, pages
IEEE Press, 2013
National Category
Computer and Information Sciences
Research subject
Technology
Identifiers
urn:nbn:se:hj:diva-43274 (URN)2-s2.0-84890850366 (Scopus ID)0;0;miljJAIL (Local ID)978-605-86311-1-3 (ISBN)0;0;miljJAIL (Archive number)0;0;miljJAIL (OAI)
Conference
16th International Conference on Information Fusion (FUSION), 2013, 9-12 July 2013, Istanbul, Turkey
Available from: 2014-01-07 Created: 2019-03-05 Last updated: 2019-08-23Bibliographically approved
Helldin, T., Falkman, G., Riveiro, M., Dahlbom, A. & Lebram, M. (2013). Transparency of military threat evaluation through visualizing uncertainty and system rationale. In: Don Harris (Ed.), Engineering Psychology and Cognitive Ergonomics: Applications and Services. Paper presented at 10th International Conference on Engineering Psychology and Cognitive Ergonomics: Applications and Services, EPCE 2013, Held as Part of 15th International Conference on Human-Computer Interaction, HCI 2013, 21 July 2013 through 26 July 2013, Las Vegas, NV (pp. 263-272). Springer
Open this publication in new window or tab >>Transparency of military threat evaluation through visualizing uncertainty and system rationale
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2013 (English)In: Engineering Psychology and Cognitive Ergonomics: Applications and Services / [ed] Don Harris, Springer, 2013, p. 263-272Conference paper, Published paper (Refereed)
Abstract [en]

Threat evaluation (TE) is concerned with determining the intent, capability and opportunity of detected targets. To their aid, military operators use support systems that analyse incoming data and make inferences based on the active evaluation framework. Several interface and interaction guidelines have been proposed for the implementation of TE systems; however there is a lack of research regarding how to make these systems transparent to their operators. This paper presents the results from interviews conducted with TE operators focusing on the need for and possibilities of improving the transparency of TE systems through the visualization of uncertainty and the presentation of the system rationale. © 2013 Springer-Verlag Berlin Heidelberg.

Place, publisher, year, edition, pages
Springer, 2013
Series
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN 0302-9743 ; 8020 LNAI
Keywords
Automation transparency, system rationale, threat evaluation, trust, uncertainty visualization, Active evaluation, Military threats, Support systems, Visualizing uncertainties, Artificial intelligence, Computer science, Transparency
National Category
Computer and Information Sciences
Research subject
Technology
Identifiers
urn:nbn:se:hj:diva-43275 (URN)10.1007/978-3-642-39354-9_29 (DOI)2-s2.0-84880759003 (Scopus ID)0;0;miljJAIL (Local ID)978-3-642-39353-2 (ISBN)978-3-642-39354-9 (ISBN)0;0;miljJAIL (Archive number)0;0;miljJAIL (OAI)
Conference
10th International Conference on Engineering Psychology and Cognitive Ergonomics: Applications and Services, EPCE 2013, Held as Part of 15th International Conference on Human-Computer Interaction, HCI 2013, 21 July 2013 through 26 July 2013, Las Vegas, NV
Available from: 2013-10-18 Created: 2019-03-05 Last updated: 2019-08-23Bibliographically approved
Projects
Synergy Virtual Ergonomics (SVE) [20180167]; University of Skövde
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-6310-346X

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