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  • 1.
    Ahlström, Christer
    et al.
    Swedish National Rd and Transport Research Institute VTI, S-58195 Linkoping, Sweden.
    Kircher, Katja
    Linköping University, Department of Behavioural Sciences and Learning, Psychology. Linköping University, Faculty of Arts and Sciences. Swedish National Rd and Transport Research Institute VTI, S-58195 Linkoping, Sweden.
    A Generalized Method to Extract Visual Time-Sharing Sequences From Naturalistic Driving Data2017In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 18, no 11, p. 2929-2938Article in journal (Refereed)
    Abstract [en]

    Indicators based on visual time-sharing have been used to investigate drivers visual behaviour during additional task execution. However, visual time-sharing analyses have been restricted to additional tasks with well-defined temporal start and end points and a dedicated visual target area. We introduce a method to automatically extract visual time-sharing sequences directly from eye tracking data. This facilitates investigations of systems, providing continuous information without well-defined start and end points. Furthermore, it becomes possible to investigate time-sharing behavior with other types of glance targets such as the mirrors. Time-sharing sequences are here extracted based on between-glance durations. If glances to a particular target are separated by less than a time-based threshold value, we assume that they belong to the same information intake event. Our results indicate that a 4-s threshold is appropriate. Examples derived from 12 drivers (about 100 hours of eye tracking data), collected in an on-road investigation of an in-vehicle information system, are provided to illustrate sequence-based analyses. This includes the possibility to investigate human-machine interface designs based on the number of glances in the extracted sequences, and to increase the legibility of transition matrices by deriving them from time-sharing sequences instead of single glances. More object-oriented glance behavior analyses, based on additional sensor and information fusion, are identified as the next future step. This would enable automated extraction of time-sharing sequences not only for targets fixed in the vehicles coordinate system, but also for environmental and traffic targets that move independently of the drivers vehicle.

  • 2.
    Ahlström, Christer
    et al.
    Swedish National Road and Transport Research Institute, Linköping, Sweden.
    Kircher, Katja
    Swedish National Road and Transport Research Institute, Linköping, Sweden.
    Kircher, Albert
    Östergötlands Läns Landsting, Heart and Medicine Center, Department of Clinical Physiology in Linköping.
    A Gaze-Based Driver Distraction Warning System and Its Effect on Visual Behavior2013In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 14, no 2, p. 965-973Article in journal (Refereed)
    Abstract [en]

    Driver distraction is a contributing factor to many crashes; therefore, a real-time distraction warning system should have the potential to mitigate or circumvent many of these crashes. The objective of this paper is to investigate the usefulness of a real-time distraction detection algorithm called AttenD. The evaluation is based on data from an extended field study comprising seven drivers who drove on an average of 4351 +/- 2181 km in a naturalistic setting. Visual behavior was investigated both on a global scale and on a local scale in the surroundings of each warning. An increase in the percentage of glances at the rear-view mirror and a decrease in the amount of glances at the center console were found. The results also show that visual time sharing decreased in duration from 9.94 to 9.20 s due to the warnings, that the time from fully attentive to warning decreased from 3.20 to 3.03 s, and that the time from warning to full attentiveness decreased from 6.02 to 5.46 s. The limited number of participants does not allow any generalizable conclusions, but a trend toward improved visual behavior could be observed. This is a promising start for further improvements of the algorithm and the warning strategy.

  • 3.
    Eidehall, Andreas
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Petersson, Lars
    National ICT Australia Ltd, Australia.
    Statistical Threat Assessment for General Road Scenes using Monte Carlo Sampling2008In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 9, no 1, p. 137-147Article in journal (Refereed)
    Abstract [en]

    This paper presents a threat-assessment algorithm for general road scenes. A road scene consists of a number of objects that are known, and the threat level of the scene is based on their current positions and velocities. The future driver inputs of the surrounding objects are unknown and are modeled as random variables. In order to capture realistic driver behavior, a dynamic driver model is implemented as a probabilistic prior, which computes the likelihood of a potential maneuver. A distribution of possible future scenarios can then be approximated using a Monte Carlo sampling. Based on this distribution, different threat measures can be computed, e.g., probability of collision or time to collision. Since the algorithm is based on the Monte Carlo sampling, it is computationally demanding, and several techniques are presented to increase performance without increasing computational load. The algorithm is intended both for online safety applications in a vehicle and for offline data analysis.

  • 4.
    Eidehall, Andreas
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Pohl, Jochen
    Volvo Car Corporation, Sweden.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Ekmark, Jonas
    Volvo Car Corporation, Sweden.
    Toward Autonomous Collision Avoidance by Steering2007In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 8, no 1, p. 84-94Article in journal (Refereed)
    Abstract [en]

    This paper presents a new automotive safety function called Emergency Lane Assist (ELA). ELA combines conventional lane guidance systems with a threat assessment module that tries to activate the lane guidance interventions according to the actual risk level of lane departure. The goal is to only prevent dangerous lane departure maneuvers. The ELA safety function is based on a statistical method that evaluates a list of safety concepts and tries to maximize the impact on accident statistics while minimizing development and hardware component costs. ELA. runs in a demonstrator and successfully intervenes during lane changes that are likely to result in a collision and is also able to take control of the vehicle and return it to a safe position in the original lane. It has also been tested on 2000 km of roads in traffic without giving any false interventions.

  • 5.
    Lundquist, Christian
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Karlsson, Rickard
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology. NIRA Dynamics AB.
    Özkan, Emre
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Tire Radii Estimation Using a Marginalized Particle Filter2014In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 15, no 2, p. 663-672Article in journal (Refereed)
    Abstract [en]

    In this paper, the measurements of individual wheel speeds and the absolute position from a global positioning system are used for high-precision estimation of vehicle tire radii. The radii deviation from its nominal value is modeled as a Gaussian random variable and included as noise components in a simple vehicle motion model. The novelty lies in a Bayesian approach to estimate online both the state vector and the parameters representing the process noise statistics using a marginalized particle filter (MPF). Field tests show that the absolute radius can be estimated with submillimeter accuracy. The approach is tested in accordance with regulation 64 of the United Nations Economic Commission for Europe on a large data set (22 tests, using two vehicles and 12 different tire sets), where tire deflations are successfully detected, with high robustness, i.e., no false alarms. The proposed MPF approach outperforms common Kalman-filter-based methods used for joint state and parameter estimation when compared with respect to accuracy and robustness.

  • 6.
    Mårtensson, Harald
    et al.
    Linköping University, Department of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Saab Aeronaut AB, S-58188 Linkoping, Sweden.
    Keelan, Oliver
    Linköping University, Department of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. ESSIQ AB, Sweden.
    Ahlström, Christer
    Swedish Natl Rd and Transport Res Inst, S-58195 Linkoping, Sweden.
    Driver Sleepiness Classification Based on Physiological Data and Driving Performance From Real Road Driving2019In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 20, no 2, p. 421-430Article in journal (Refereed)
    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 amp;lt;= 6) or sleepy (KSS amp;gt;= 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.

  • 7.
    Wahlström, Niklas
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Hostettler, Roland
    Luleå University of Technology, Division of Systems and Interaction.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Birk, Wolfgang
    Luleå University of Technology, Division of Systems and Interaction.
    Classification of Driving Direction in Traffic Surveillance using Magnetometers2014In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 15, no 4, p. 1405-1418Article in journal (Refereed)
    Abstract [en]

    We present an approach for computing the driving direction of a vehicle by processing measurements from one 2-axis magnetometer. The proposed method relies on a non-linear transformation of the measurement data comprising only two inner products. Deterministic analysis of the signal model reveals how the driving direction affects the measurement signal and the proposed classifier is analyzed in terms of its statistical properties. The method is compared with a model based likelihood test using both simulated and experimental data. The experimental verification indicates that good performance is achieved under the presence of saturation, measurement noise, and near field effects.

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