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Liu, Z., Ahlström, C., Forsman, Å. & Kircher, K. (2019). Attentional Demand as a Function of Contextual Factors in Different Traffic Scenarios. Human Factors, Article ID UNSP 0018720819869099.
Open this publication in new window or tab >>Attentional Demand as a Function of Contextual Factors in Different Traffic Scenarios
2019 (English)In: Human Factors, ISSN 0018-7208, E-ISSN 1547-8181, article id UNSP 0018720819869099Article in journal (Refereed) Published
Abstract [en]

Objective: To assess the attentional demand of different contextual factors in driving.

Background: The attentional demand on the driver varies with the situation. One approach for estimating the attentional demand, via spare capacity, is to use visual occlusion.

Method: Using a 3 × 5 within-subjects design, 33 participants drove in a fixed-base simulator in three scenarios (i.e., urban, rural, and motorway), combined with five fixed occlusion durations (1.0, 1.4, 1.8, 2.2, and 2.6 s). By pressing a microswitch on a finger, the driver initiated each occlusion, which lasted for the same predetermined duration within each trial. Drivers were instructed to occlude their vision as often as possible while still driving safely.

Results: Stepwise logistic regression per scenario indicated that the occlusion predictors varied with scenario. In the urban environment, infrastructure-related variables had the biggest influence, whereas the distance to oncoming traffic played a major role on the rural road. On the motorway, occlusion duration and time since the last occlusion were the main determinants.

Conclusion: Spare capacity is dependent on the scenario, selected speed, and individual factors. This is important for developing workload managers, infrastructural design, and aspects related to transfer of control in automated driving.

Application: Better knowledge of the determinants of spare capacity in the road environment can help improve workload managers, thereby contributing to more efficient and safer interaction with additional tasks.

Place, publisher, year, edition, pages
SAGE PUBLICATIONS INC, 2019
National Category
Applied Psychology
Identifiers
urn:nbn:se:vti:diva-14118 (URN)10.1177/0018720819869099 (DOI)000483617400001 ()31424969 (PubMedID)2-s2.0-85071458285 (Scopus ID)
Available from: 2019-10-01 Created: 2019-10-01 Last updated: 2019-10-01Bibliographically approved
Barua, S., Ahmed, M. U., Ahlström, C. & Begum, S. (2019). Automatic driver sleepiness detection using EEG, EOG and contextual information. Expert systems with applications, 115, 121-135
Open this publication in new window or tab >>Automatic driver sleepiness detection using EEG, EOG and contextual information
2019 (English)In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 115, p. 121-135Article in journal (Refereed) Published
Abstract [en]

The many vehicle crashes that are caused by driver sleepiness each year advocates the development of automated driver sleepiness detection (ADSD) systems. This study proposes an automatic sleepiness classification scheme designed using data from 30 drivers who repeatedly drove in a high-fidelity driving simulator, both in alert and in sleep deprived conditions. Driver sleepiness classification was performed using four separate classifiers: k-nearest neighbours, support vector machines, case-based reasoning, and random forest, where physiological signals and contextual information were used as sleepiness indicators. The subjective Karolinska sleepiness scale (KSS) was used as target value. An extensive evaluation on multiclass and binary classifications was carried out using 10-fold cross-validation and leave-one-out validation. With 10-fold cross-validation, the support vector machine showed better performance than the other classifiers (79% accuracy for multiclass and 93% accuracy for binary classification). The effect of individual differences was also investigated, showing a 10% increase in accuracy when data from the individual being evaluated was included in the training dataset. Overall, the support vector machine was found to be the most stable classifier. The effect of adding contextual information to the physiological features improved the classification accuracy by 4% in multiclass classification and by and 5% in binary classification.

Place, publisher, year, edition, pages
Elsevier Ltd, 2019
Keywords
Fatigue (human), Classification, Driving aid (electronic), Detection, Development, Simulator (driving)
National Category
Transport Systems and Logistics
Research subject
90 Road: Vehicles and vehicle technology, 914 Road: ITS och vehicle technology
Identifiers
urn:nbn:se:vti:diva-13239 (URN)10.1016/j.eswa.2018.07.054 (DOI)2-s2.0-85051410923 (Scopus ID)
Available from: 2018-09-12 Created: 2018-09-12 Last updated: 2018-12-19Bibliographically approved
Oliveira, L., Cardoso, J. S., Lourenço, A. & Ahlström, C. (2019). Driver drowsiness detection: A comparison between intrusive and non-intrusive signal acquisition methods. In: Proceedings - European Workshop on Visual Information Processing, EUVIP: . Paper presented at 7th European Workshop on Visual Information Processing, EUVIP 2018, 26 November 2018 through 28 November 2018. Institute of Electrical and Electronics Engineers Inc., Article ID 8611704.
Open this publication in new window or tab >>Driver drowsiness detection: A comparison between intrusive and non-intrusive signal acquisition methods
2019 (English)In: Proceedings - European Workshop on Visual Information Processing, EUVIP, Institute of Electrical and Electronics Engineers Inc. , 2019, article id 8611704Conference paper, Published paper (Refereed)
Abstract [en]

Driver drowsiness is a major cause of road accidents, many of which result in fatalities. A solution to this problem is the inclusion of a drowsiness detector in vehicles to alert the driver if sleepiness is detected. To detect drowsiness, physiologic, behavioral (visual) and vehicle-based methods can be used, however, only measures that can be acquired non-intrusively are viable in a real life application. This work uses data from a real-road experiment with sleep deprived drivers to compare the performance of driver drowsiness detection using intrusive acquisition methods, namely electrooculogram (EOG), with camera-based, non-intrusive, methods. A hybrid strategy, combining the described methods with electrocardiogram (ECG) measures, is also evaluated. Overall, the obtained results show that drowsiness detection performance is similar using non-intrusive camera-based measures or intrusive EOG measures. The detection performance increases when combining two methods (ECG + visual) or (ECG + EOG).

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2019
Keywords
Fatigue (human), Detection, Driver, Measurement, Eye movement, Camera, ECG
National Category
Applied Psychology
Research subject
80 Road: Traffic safety and accidents, 841 Road: Road user behaviour; 90 Road: Vehicles and vehicle technology, 914 Road: ITS och vehicle technology
Identifiers
urn:nbn:se:vti:diva-13724 (URN)10.1109/EUVIP.2018.8611704 (DOI)2-s2.0-85062709894 (Scopus ID)9781538668979 (ISBN)
Conference
7th European Workshop on Visual Information Processing, EUVIP 2018, 26 November 2018 through 28 November 2018
Available from: 2019-05-02 Created: 2019-05-02 Last updated: 2019-06-27Bibliographically approved
Mårtensson, H., Keelan, O. & Ahlström, C. (2019). Driver Sleepiness Classification Based on Physiological Data and Driving Performance from Real Road Driving. IEEE transactions on intelligent transportation systems (Print), 20(2), 421-430, Article ID 8331164.
Open this publication in new window or tab >>Driver Sleepiness Classification Based on Physiological Data and Driving Performance from Real Road Driving
2019 (English)In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 20, no 2, p. 421-430, article id 8331164Article in journal (Refereed) Published
Abstract [en]

The objective of this paper is to investigate if signal analysis and machine learning can be used to develop an accurate sleepiness warning system. The developed system was trained using the supposedly most reliable sleepiness indicators available, extracted from electroencephalography, electrocardiography, electrooculography, and driving performance data (steering behavior and lane positioning). Sequential forward floating selection was used to select the most descriptive features, and five different classifiers were tested. A unique data set with 86 drivers, obtained while driving on real roads in real traffic, both in alert and sleep deprived conditions, was used to train and test the classifiers. Subjective ratings using the Karolinska sleepiness scale (KSS) was used to split the data as sufficiently alert (KSS ≤ 6) or sleepy (KSS ≥ 8). KSS = 7 was excluded to get a clearer distinction between the groups. A random forest classifier was found to be the most robust classifier with an accuracy of 94.1% (sensitivity 86.5%, specificity 95.7%). The results further showed the importance of personalizing a sleepiness detection system. When testing the classifier on data from a person that it had not been trained on, the sensitivity dropped to 41.4%. One way to improve the sensitivity was to add a biomathematical model of sleepiness amongst the features, which increased the sensitivity to 66.2% for participant-independent classification. Future works include taking contextual features into account, using classifiers that takes full advantage of sequential data, and to develop models that adapt to individual drivers.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2019
Keywords
Driver, Fatigue (human), Classification, Warning, Eye movement, EEG, Electrocardiography, Steering (process), Traffic lane, Behaviour
National Category
Applied Psychology
Research subject
80 Road: Traffic safety and accidents, 84 Road: Road users
Identifiers
urn:nbn:se:vti:diva-13650 (URN)10.1109/TITS.2018.2814207 (DOI)000460756900002 ()2-s2.0-85061121994 (Scopus ID)
Available from: 2019-05-17 Created: 2019-05-17 Last updated: 2019-06-27Bibliographically approved
Silveira, C. S., Cardoso, J. S., Lourenco, A. L. & Ahlström, C. (2019). Importance of subject-dependent classification and imbalanced distributions in driver sleepiness detection in realistic conditions. IET Intelligent Transport Systems, 13(2), 347-355
Open this publication in new window or tab >>Importance of subject-dependent classification and imbalanced distributions in driver sleepiness detection in realistic conditions
2019 (English)In: IET Intelligent Transport Systems, ISSN 1751-956X, E-ISSN 1751-9578, Vol. 13, no 2, p. 347-355Article in journal (Refereed) Published
Abstract [en]

The first in-depth study on the use of electrocardiogram and electrooculogram for subject-dependent classification in driver sleepiness/fatigue under realistic driving conditions is presented in this work. Since acquisitions in simulated environments may be misleading for sleepiness assessment, performing studies on road are required. For that purpose, the authors present a database resulting from a field driving study performed in the SleepEye project. Based on previous research, supervised machine learning methods are implemented and applied to 16 heart- and 25 eye-based extracted features, mostly related to heart rate variability and blink events, respectively, in order to study the influence of subject dependency in sleepiness classification, using different classifiers and dealing with imbalanced class distributions. Results showed a significantly worse performance in subject-independent classification: a decrease of similar to 40 and 20% in the detection rate of the 'sleepy' class for two and three classes, respectively. Since physiological signals are the ones that present the most individual characteristics, a subject-independent classification can be even harder to perform. Transfer learning techniques and methods for imbalanced distributions are promising approaches and need further investigation.

Place, publisher, year, edition, pages
INST ENGINEERING TECHNOLOGY-IET, 2019
Keywords
Fatigue (human), Driver, Driver, Classification, Electrocardiography, Eye movement, In situ, Driving (veh)
National Category
Medical Laboratory and Measurements Technologies
Research subject
80 Road: Traffic safety and accidents, 84 Road: Road users
Identifiers
urn:nbn:se:vti:diva-13596 (URN)10.1049/iet-its.2018.5284 (DOI)000457717800013 ()2-s2.0-85061325453 (Scopus ID)
Available from: 2019-05-17 Created: 2019-05-17 Last updated: 2019-06-27Bibliographically approved
Kircher, K., Kujala, T. & Ahlström, C. (2019). On the Difference Between Necessary and Unnecessary Glances Away From the Forward Roadway: An Occlusion Study on the Motorway. Human Factors, Article ID UNSP 0018720819866946.
Open this publication in new window or tab >>On the Difference Between Necessary and Unnecessary Glances Away From the Forward Roadway: An Occlusion Study on the Motorway
2019 (English)In: Human Factors, ISSN 0018-7208, E-ISSN 1547-8181, article id UNSP 0018720819866946Article in journal (Refereed) Published
Abstract [en]

Objective: The present study strove to distinguish traffic-related glances away from the forward roadway from non-traffic-related glances while assessing the minimum amount of visual information intake necessary for safe driving in particular scenarios.

Background: Published gaze-based distraction detection algorithms and guidelines for distraction prevention essentially measure the time spent looking away from the forward roadway, without incorporating situation-based attentional requirements. Incorporating situation-based attentional requirements would entail an approach that not only considers the time spent looking elsewhere but also checks whether all necessary information has been sampled.

Method: We assess the visual sampling requirements for the forward view based on 25 experienced drivers’ self-paced visual occlusion in real motorway traffic, dependent on a combination of situational factors, and compare these with their corresponding glance behavior in baseline driving.

Results: Occlusion durations were on average 3 times longer than glances away from the forward roadway, and they varied substantially depending on particular maneuvers and on the proximity of other traffic, showing that interactions with nearby traffic increase perceived uncertainty. The frequency of glances away from the forward roadway was relatively stable across proximity levels and maneuvers, being very similar to what has been found in naturalistic driving.

Conclusion: Glances away from the forward roadway proved qualitatively different from occlusions in both their duration and when they occur. Our findings indicate that glancing away from the forward roadway for driving purposes is not the same as glancing away for other purposes, and that neither is necessarily equivalent to distraction.

Place, publisher, year, edition, pages
SAGE PUBLICATIONS INC, 2019
National Category
Applied Psychology
Identifiers
urn:nbn:se:vti:diva-14119 (URN)10.1177/0018720819866946 (DOI)000482366500001 ()31403323 (PubMedID)2-s2.0-85071486075 (Scopus ID)
Available from: 2019-10-01 Created: 2019-10-01 Last updated: 2019-10-01Bibliographically approved
Ahlström, C., Wachtmeister, J., Nyman, M., Nordenström, A. & Kircher, K. (2019). Using smartphone logging to gain insight about phone use in traffic. Cognition, Technology & Work
Open this publication in new window or tab >>Using smartphone logging to gain insight about phone use in traffic
Show others...
2019 (English)In: Cognition, Technology & Work, ISSN 1435-5558, E-ISSN 1435-5566Article in journal (Refereed) Published
Abstract [en]

The prevalence of mobile phone usage in traffic has been studied by road-side counting, naturalistic driving data, surveillance cameras, smartphone logging, and subjective estimates via surveys. Here, we describe a custom-made smartphone logging application along with suggestions on how future such applications should be designed. The developed application logs’ start and end times of all phone interactions (mobile phone applications, incoming/outgoing phone calls and text messages, audio output, and screen activations). In addition, all movements are automatically classified into transport, cycling, walking, running, or stationary. The capabilities of the approach are demonstrated in a pilot study with 143 participants. Examples of results that can be gained from smartphone logging include prevalence in different transportation modes (here found to be 12% while driving, 4% while cycling, and 7% while walking), which apps are being used (here found to be 19% navigation, 12% talking, 12% social media, and 10% games) and on which road types (rural, urban, highway etc.). Smartphone logging was found to be an insightful complement to the other methods for assessing phone use in traffic, especially since it allows the analyses of which apps are used and where they are used, split into transportation mode and road type, all at a relatively low cost.

Place, publisher, year, edition, pages
Springer London, 2019
Keywords
Mobile phone, Use, Data acquisition, Mobile application, Transport mode
National Category
Applied Psychology
Research subject
80 Road: Traffic safety and accidents, 841 Road: Road user behaviour
Identifiers
urn:nbn:se:vti:diva-13654 (URN)10.1007/s10111-019-00547-6 (DOI)2-s2.0-85061633847 (Scopus ID)
Available from: 2019-05-17 Created: 2019-05-17 Last updated: 2019-06-27Bibliographically approved
Anund, A., Ahlström, C., Fors, C. & Åkerstedt, T. (2018). Are professional drivers less sleepy than non-professional drivers?. Scandinavian Journal of Work, Environment and Health, 44(1), 88-95
Open this publication in new window or tab >>Are professional drivers less sleepy than non-professional drivers?
2018 (English)In: Scandinavian Journal of Work, Environment and Health, ISSN 0355-3140, E-ISSN 1795-990X, Vol. 44, no 1, p. 88-95Article in journal (Refereed) Published
Abstract [en]

Objective It is generally believed that professional drivers can manage quite severe fatigue before routine driving performance is affected. In addition, there are results indicating that professional drivers can adapt to prolonged night shifts and may be able to learn to drive without decreased performance under high levels of sleepiness. However, very little research has been conducted to compare professionals and non-professionals when controlling for time driven and time of day.

Method The aim of this study was to use a driving simulator to investigate whether professional drivers are more resistant to sleep deprivation than non-professional drivers. Differences in the development of sleepiness (self-reported, physiological and behavioral) during driving was investigated in 11 young professional and 15 non-professional drivers.

Results Professional drivers self-reported significantly lower sleepiness while driving a simulator than nonprofessional drivers. In contradiction, they showed longer blink durations and more line crossings, both of which are indicators of sleepiness. They also drove faster. The reason for the discrepancy in the relation between the different sleepiness indicators for the two groups could be due to more experience to sleepiness among the professional drivers or possibly to the faster speed, which might unconsciously have been used by the professionals to try to counteract sleepiness.

Conclusion Professional drivers self-reported significantly lower sleepiness while driving a simulator than non-professional drivers. However, they showed longer blink durations and more line crossings, both of which are indicators of sleepiness, and they drove faster.

Place, publisher, year, edition, pages
SCANDINAVIAN JOURNAL WORK ENVIRONMENT & HEALTH, 2018
Keywords
Fatigue (human), Driver, Professional category, Simulator (driving), Speed, Behaviour
National Category
Applied Psychology
Research subject
80 Road: Traffic safety and accidents, 841 Road: Road user behaviour
Identifiers
urn:nbn:se:vti:diva-12735 (URN)10.5271/sjweh.3677 (DOI)000418916600010 ()29018866 (PubMedID)2-s2.0-85040066980 (Scopus ID)
Available from: 2018-05-17 Created: 2018-05-17 Last updated: 2018-06-12Bibliographically approved
Nygårdhs, S., Ahlström, C., Ihlström, J. & Kircher, K. (2018). Bicyclists' adaptation strategies when interacting with text messages in urban environments. Cognition, Technology & Work, 20(3), 377-388
Open this publication in new window or tab >>Bicyclists' adaptation strategies when interacting with text messages in urban environments
2018 (English)In: Cognition, Technology & Work, ISSN 1435-5558, E-ISSN 1435-5566, Vol. 20, no 3, p. 377-388Article in journal (Refereed) Published
Abstract [en]

Cyclists' use of mobile phones in traffic has typically been studied in controlled experiments. How cyclists adapt their behaviour when they are not limited to a certain set of behaviours has not been investigated to any large extent. The aims of this study are to explore how cyclists adapt when texting and listening to music in a complex urban environment, and if they compensate sufficiently to maintain safe traffic behaviour. Forty-one cyclists participated in a semi-controlled study, using their own bike and smartphone in real traffic. They were equipped with eye tracking glasses and travelled two laps completing a total of 6 km divided into six segments. For one of the laps, the cyclists were requested to listen to music. On three occasions, they received a text message to their phone, which they were supposed to handle as they normally would when cycling. Static minimum required attention measures were used to examine the influence on attention. The results show that listening to music while cycling did not affect workload, speed, SMS interaction or attention. Seven different adaptation behaviours were identified when the cyclists dealt with received text messages. One-fourth of the text messages were replied to while cycling. In general, the cyclists manage to integrate SMS interactions with their cycling behaviour. Nevertheless, there were two occasions when basic attention criteria were violated while texting, which motivate further studies.

Place, publisher, year, edition, pages
SPRINGER LONDON LTD, 2018
Keywords
Cyclist, Behaviour, Mobile phone, Use, Cycling, Attention, Mental load
National Category
Human Aspects of ICT
Research subject
80 Road: Traffic safety and accidents, 841 Road: Road user behaviour
Identifiers
urn:nbn:se:vti:diva-13185 (URN)10.1007/s10111-018-0478-y (DOI)000439906300004 ()2-s2.0-85045467252 (Scopus ID)
Available from: 2018-09-12 Created: 2018-09-12 Last updated: 2018-11-19Bibliographically approved
Kircher, K., Ihlström, J., Nygårdhs, S. & Ahlström, C. (2018). Cyclist efficiency and its dependence on infrastructure and usual speed. Transportation Research Part F: Traffic Psychology and Behaviour, 54, 148-158
Open this publication in new window or tab >>Cyclist efficiency and its dependence on infrastructure and usual speed
2018 (English)In: Transportation Research Part F: Traffic Psychology and Behaviour, ISSN 1369-8478, E-ISSN 1873-5517, Vol. 54, p. 148-158Article in journal (Refereed) Published
Abstract [en]

Bicyclists are a heterogeneous group, with varying abilities, traffic education and experience. While efficiency was identified as an important factor on utility bicycle trips, it might be traded for experienced safety, for example by choosing different pathways in a given situation, or by relinquishing one's right of way. In a semi-controlled study with 41 participants, a grouping was made according to self-reported riding speed in relation to other cyclists. The participants cycled twice along a 3 km inner-city route, passing four intersections with different priority rules. The cyclists were free to choose how to negotiate the intersections. Speed and the traffic surroundings were recorded via gps and cameras on the bike of the participant and of a following experimenter. For each cyclist, the ‘base’ speed on undisturbed segments was determined as reference. Based on this, the efficiency in different types of intersections was computed per cyclist group. It turned out that infrastructural aspects, cyclist group and the presence and behaviour of interacting traffic influenced cyclist efficiency. Faster cyclists were delayed more when the infrastructure required a stop regardless of the traffic situation, like at a red traffic light or a stop sign. The members of the so-called ‘comfort cyclists’ group were delayed the most in a roundabout with mixed traffic, where many chose to get off their bike and walk. In a society working for equality of access to the transport system, it is recommended to develop solutions that consider and accommodate the behaviours of different cyclist groups when planning bicycling infrastructure.

Place, publisher, year, edition, pages
Elsevier Ltd, 2018
Keywords
Cyclist, Behaviour, Junction, Traffic mixture, Design (overall design), Efficiencey, Journey
National Category
Infrastructure Engineering
Research subject
10 Road: Transport, society, policy and planning, 113 Road: Cycling, walking and moped transport
Identifiers
urn:nbn:se:vti:diva-12861 (URN)10.1016/j.trf.2018.02.002 (DOI)2-s2.0-85042191714 (Scopus ID)
Available from: 2018-04-05 Created: 2018-04-05 Last updated: 2018-05-21Bibliographically approved
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ORCID iD: ORCID iD iconorcid.org/0000-0003-4134-0303

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