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  • 1.
    Aramrattana, Maytheewat
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Centre for Research on Embedded Systems (CERES). The Swedish National Road and Transport Research Institute (VTI), Linköping, Sweden.
    Detournay, Jérôme
    Halmstad University, School of Information Technology.
    Englund, Cristofer
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research. RISE Viktoria, Gothenburg, Sweden.
    Frimodig, Victor
    Halmstad University, School of Information Technology.
    Uddman Jansson, Oscar
    Halmstad University, School of Information Technology.
    Larsson, Tony
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Centre for Research on Embedded Systems (CERES).
    Mostowski, Wojciech
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Centre for Research on Embedded Systems (CERES).
    Díez Rodríguez, Víctor
    Halmstad University, School of Information Technology.
    Rosenstatter, Thomas
    The Swedish National Road and Transport Research Institute (VTI), Linköping, Sweden.
    Shahanoor, Golam
    Halmstad University, School of Information Technology.
    Mastering Cooperative Driving Challenges in a Competition Scenario2017In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016Article in journal (Refereed)
  • 2.
    Belyaev, Evgeny
    et al.
    Department of Signal Processing, Tampere University of Technology, Tampere, Finland.
    Molchanov, Pavlo
    Department of Signal Processing, Tampere University of Technology, Tampere, Finland.
    Vinel, Alexey
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Centre for Research on Embedded Systems (CERES). Department of Electronics and Communication Engineering, Tampere University of Technology, Tampere, Finland.
    Koucheryavy, Yevgeni
    Department of Electronics and Communication Engineering, Tampere University of Technology, Tampere, Finland.
    The Use of Automotive Radars in Video-Based Overtaking Assistance Applications2013In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 14, no 3, p. 1035-1042, article id 649464Article in journal (Refereed)
    Abstract [en]

    Overtaking on rural roads may cause severe accidents when oncoming traffic is detected by a driver too late, or its speed is underestimated. Recently proposed cooperative overtaking assistance systems are based on real-time video transmission, where a video stream captured with a camera installed at the windshield of a vehicle is compressed, broadcast through the wireless channel, and displayed to the drivers of vehicles driving behind. In such a system, it is of ultimate importance to deliver video information about the opposite lane with low end-to-end latency and good visual quality. In this paper, we propose reallocating the wireless channel resources in favor of the part of the captured video frame containing the image of the oncoming vehicle. To achieve this goal, we apply automotive radar for oncoming vehicle detection, and we use the image of this vehicle as a region-of-interest (ROI) for the video rate control. We present the theoretical framework, which describes the basics of such an approach and can serve as a useful guideline for the future practical implementation of the overtaking assistance systems. The benefits of our proposal are demonstrated in relation to the practical scenario of H.264/Advance Video Coding (AVC), IEEE 802.11p/Wireless Access for Vehicular Environments (WAVE) intervehicle communication standards, and currently used automotive radars.

  • 3.
    Chen, Lei
    et al.
    Viktoria Swedish ICT, Gothenburg, Sweden.
    Englund, Cristofer
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research. Viktoria Swedish ICT, Gothenburg, Sweden & SAFER Vehicle and Traffic Safety Centre, Chalmers University of Technology, Gothenburg.
    Cooperative Intersection Management: A Survey2016In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 17, no 2, p. 570-586Article, review/survey (Refereed)
    Abstract [en]

    Intersection management is one of the most challenging problems within the transport system. Traffic light-based methods have been efficient but are not able to deal with the growing mobility and social challenges. On the other hand, the advancements of automation and communications have enabled cooperative intersection management, where road users, infrastructure, and traffic control centers are able to communicate and coordinate the traffic safely and efficiently. Major techniques and solutions for cooperative intersections are surveyed in this paper for both signalized and nonsignalized intersections, whereas focuses are put on the latter. Cooperative methods, including time slots and space reservation, trajectory planning, and virtual traffic lights, are discussed in detail. Vehicle collision warning and avoidance methods are discussed to deal with uncertainties. Concerning vulnerable road users, pedestrian collision avoidance methods are discussed. In addition, an introduction to major projects related to cooperative intersection management is presented. A further discussion of the presented works is given with highlights of future research topics. This paper serves as a comprehensive survey of the field, aiming at stimulating new methods and accelerating the advancement of automated and cooperative intersections. © 2015 IEEE.

  • 4.
    Díez Rodríguez, Victor
    et al.
    Halmstad University, School of Information Technology.
    Detournay, Jérôme
    Halmstad University, School of Information Technology.
    Vinel, Alexey
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Centre for Research on Embedded Systems (CERES).
    Lyamin, Nikita
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Centre for Research on Embedded Systems (CERES).
    An Approach for Receiver-Side Awareness Control in Vehicular Ad Hoc Networks2018In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 19, no 4, p. 1227-1236Article in journal (Refereed)
    Abstract [en]

    Vehicular Ad hoc networks (VANET) are a key element of cooperative intelligent transport systems. One of the challenges in VANETs is dealing with awareness and congestion due to the high amount of messages received from the vehicles in communication range. As VANETs are used in critical applications, congestion on the receiver side caused by the buffering of the packets is a safety hazard. In this paper, we propose a streamwise queuing system on the receiver side and show how it improves the timeliness of the messages received and maintains the awareness of the system in a congestion situation. © Copyright 2017 IEEE

  • 5.
    Lidström, Kristoffer
    et al.
    Viktoria Institute, Göteborg, Sweden.
    Sjöberg, Katrin
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Centre for Research on Embedded Systems (CERES).
    Holmberg, Ulf
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Centre for Research on Embedded Systems (CERES).
    Andersson, Johan
    Volvo Car Corporation, Göteborg, Sweden.
    Bergh, Fredrik
    Cybercom Group, Stockholm, Sweden.
    Bjäde, Mattias
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE).
    Mak, Spencer
    Innovation Team, Halmstad, Sweden.
    A modular CACC system integration and design2012In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 13, no 3, p. 1050-1061Article in journal (Refereed)
    Abstract [en]

    This paper describes the Halmstad University entry in the Grand Cooperative Driving Challenge, which is a competition in vehicle platooning. Cooperative platooning has the potential to improve traffic flow by mitigating shock wave effects, which otherwise may occur in dense traffic. A longitudinal controller that uses information exchanged via wireless communication with other cooperative vehicles to achieve string-stable platooning is developed. The controller is integrated into a production vehicle, together with a positioning system, communication system, and human–machine interface (HMI). A highly modular system architecture enabled rapid development and testing of the various subsystems. In the competition, which took place in May 2011 on a closed-off highway in The Netherlands, the Halmstad University team finished second among nine competing teams.

  • 6.
    Ploeg, Jeroen
    et al.
    TNO, Helmond, The Netherlands & Eindhoven University of Technology, Eindhoven, Netherlands.
    Englund, Cristofer
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Nijmeijer, Henk
    Eindhoven University of Technology, Eindhoven, Netherlands.
    Semsar-Kazerooni, Elham
    TNO, Helmond, The Netherlands & Twente University, Enschede, The Netherlands.
    Shladover, Steven E.
    TRB Committee on Vehicle-Highway Automation, California PATH Program, Institute of Transportation Studies, University of California, Berkeley, CA, USA.
    Voronov, Alexey
    RISE Viktoria, Gothenburg, Sweden.
    van de Wouw, Nathan
    Eindhoven University of Technology, Eindhoven, Netherlands & University of Minnesota, Minneapolis, Minnesota, USA & Delft University of Technology, Delft, The Netherlands.
    Guest Editorial Introduction to the Special Issue on the 2016 Grand Cooperative Driving Challenge2018In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 19, no 4, p. 1208-1212Article in journal (Refereed)
    Abstract [en]

    Cooperative driving is based on wireless communications between vehicles and between vehicles and roadside infrastructure, aiming for increased traffic flow and traffic safety, while decreasing fuel consumption and emissions. To support and accelerate the introduction of cooperative vehicles in everyday traffic, in 2011, nine international teams joined the Grand Cooperative Driving Challenge (GCDC). The challenge was to perform platooning, in which vehicles drive in road trains with short intervehicle distances. The results were reported in a Special Issue of IEEE Transactions on Intelligent Transportation Systems, published in September 2012 [item 1 in the Appendix]. © 2000-2011 IEEE.

  • 7.
    Ploeg, Jeroen
    et al.
    Netherlands Organisation for Applied Scientific Research TNO, Helmond, The Netherlands.
    Semsar-Kazerooni, Elham
    Netherlands Organisation for Applied Scientific Research TNO, Helmond, The Netherlands.
    Morales Medina, Alejandro I.
    Eindhoven University of Technology, Eindhoven, The Netherlands.
    de Jongh, Jan F. C. M
    Netherlands Organisation for Applied Scientific Research TNO, Helmond, The Netherlands.
    van de Sluis, Jacco
    Netherlands Organisation for Applied Scientific Research TNO, Helmond, The Netherlands.
    Voronov, Alexey
    RISE Viktoria, Gothenburg, Sweden.
    Englund, Cristofer
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS). RISE Viktoria, Gothenburg, Sweden & Chalmers University of Technology, Gothenburg, Sweden.
    Bril, Reinder J.
    Eindhoven University of Technology, Eindhoven, The Netherlands.
    Salunkhe, Hrishikesh
    Thermo Fisher Scientific, Eindhoven, The Netherlands.
    Arrú, Álvaro
    Applus+ IDIADA, L'Albornar, Tarragona, Spain.
    Ruano, Aitor
    Applus+ IDIADA, L'Albornar, Tarragona, Spain.
    Garcí-Sol, Lorena
    Applus+ IDIADA, L'Albornar, Tarragona, Spain.
    van Nunen, Ellen
    Netherlands Organisation for Applied Scientific Research TNO, Helmond, The Netherlands.
    van de Wouw, Nathan
    Eindhoven University of Technology, Eindhoven, The Netherlands; University of Minnesota, Minneapolis, MN, USA & Delft University of Technology, Delft, The Netherlands.
    Cooperative Automated Maneuvering at the 2016 Grand Cooperative Driving Challenge2018In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 19, no 4, p. 1213-1226Article in journal (Refereed)
    Abstract [en]

    Cooperative adaptive cruise control and platooning are well-known applications in the field of cooperative automated driving. However, extension toward maneuvering is desired to accommodate common highway maneuvers, such as merging, and to enable urban applications. To this end, a layered control architecture is adopted. In this architecture, the tactical layer hosts the interaction protocols, describing the wireless information exchange to initiate the vehicle maneuvers, supported by a novel wireless message set, whereas the operational layer involves the vehicle controllers to realize the desired maneuvers. This hierarchical approach was the basis for the Grand Cooperative Driving Challenge (GCDC), which was held in May 2016 in The Netherlands. The GCDC provided the opportunity for participating teams to cooperatively execute a highway lane-reduction scenario and an urban intersection-crossing scenario. The GCDC was set up as a competition and, hence, also involving assessment of the teams' individual performance in a cooperative setting. As a result, the hierarchical architecture proved to be a viable approach, whereas the GCDC appeared to be an effective instrument to advance the field of cooperative automated driving. © Copyright 2017 IEEE - All rights reserved.

  • 8.
    Rosenstatter, Thomas
    et al.
    Chalmers University, Gothenburg, Sweden.
    Englund, Cristofer
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research. RISE Viktoria, Gothenburg, Sweden.
    Modelling the Level of Trust in a Cooperative Automated Vehicle Control System2018In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 19, no 4, p. 1237-1247Article in journal (Refereed)
    Abstract [en]

    Vehicle-to-vehicle communication is a key technology for achieving increased perception for automated vehicles, where the communication enables virtual sensing by means of sensors in other vehicles. In addition, this technology also allows detection and recognition of objects that are out-of-sight. This paper presents a trust system that allows a cooperative and automated vehicle to make more reliable and safe decisions. The system evaluates the current situation and generates a trust index indicating the level of trust in the environment, the ego vehicle, and the surrounding vehicles. This research goes beyond secure communication and concerns the verification of the received data on a system level. The results show that the proposed method is capable of correctly identifying various traffic situations and how the trust index is used while manoeuvring in a platoon merge scenario. © Copyright 2017 IEEE - All rights reserved.

  • 9.
    Vaiciukynas, Evaldas
    et al.
    Department of Information Systems, Kaunas University of Technology, Kaunas, Lithuania.
    Uličný, Matej
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Pashami, Sepideh
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Nowaczyk, Sławomir
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Learning Low-Dimensional Representation of Bivariate Histogram Data2018In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016Article in journal (Refereed)
    Abstract [en]

    With an increasing amount of data in intelligent transportation systems, methods are needed to automatically extract general representations that accurately predict not only known tasks but also similar tasks that can emerge in the future. Creation of low-dimensional representations can be unsupervised or can exploit various labels in multi-task learning (when goal tasks are known) or transfer learning (when they are not) settings. Finding a general, low-dimensional representation suitable for multiple tasks is an important step toward knowledge discovery in aware intelligent transportation systems. This paper evaluates several approaches mapping high-dimensional sensor data from Volvo trucks into a low-dimensional representation that is useful for prediction. Original data are bivariate histograms, with two types--turbocharger and engine--considered. Low-dimensional representations were evaluated in a supervised fashion by mean equal error rate (EER) using a random forest classifier on a set of 27 1-vs-Rest detection tasks. Results from unsupervised learning experiments indicate that using an autoencoder to create an intermediate representation, followed by $t$-distributed stochastic neighbor embedding, is the most effective way to create low-dimensional representation of the original bivariate histogram. Individually, $t$-distributed stochastic neighbor embedding offered best results for 2-D or 3-D and classical autoencoder for 6-D or 10-D representations. Using multi-task learning, combining unsupervised and supervised objectives on all 27 available tasks, resulted in 10-D representations with a significantly lower EER compared to the original 400-D data. In transfer learning setting, with topmost diverse tasks used for representation learning, 10-D representations achieved EER comparable to the original representation.

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