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  • 1.
    Alam, Assad
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Asplund, Fredrik
    KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Mechatronics.
    Behere, Sagar
    KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Mechatronics.
    Björk, Mattias
    KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Mechatronics.
    Garcia Alonso, Liliana
    KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Mechatronics.
    Khaksari, Farzad
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Khan, Altamash
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Kjellberg, Joakim
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Liang, Kuo-Yun
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Lyberger, Rickard
    Scania CV AB.
    Mårtensson, Jonas
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Nilsson, John-Olof
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Pettersson, Henrik
    Scania CV AB.
    Pettersson, Simon
    KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Mechatronics.
    Stålklinga, Elin
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Sundman, Dennis
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Zachariah, Dave
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Cooperative driving according to Scoop2011Report (Other academic)
    Abstract [en]

    KTH Royal Institute of Technology and Scania are entering the GCDC 2011 under the name Scoop –Stockholm Cooperative Driving. This paper is an introduction to their team and to the technical approach theyare using in their prototype system for GCDC 2011.

  • 2.
    Dán, György
    et al.
    KTH, School of Electrical Engineering (EES), Communication Networks. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Khan, Muhammadaltamash A.
    KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Fodor, Viktoria
    KTH, School of Electrical Engineering (EES), Communication Networks. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Characterization of SURF and BRISK interest point distribution for distributed feature extraction in visual sensor networks2015In: IEEE transactions on multimedia, ISSN 1520-9210, E-ISSN 1941-0077, Vol. 17, no 5, 591-602 p., 7047857Article in journal (Refereed)
    Abstract [en]

    We study the statistical characteristics of SURF and BRISK interest points and descriptors, with the aim of supporting the design of distributed processing across sensor nodes in a resource -constrained visual sensor network (VSN). Our results show high variability in the density, the spatial distribution , and the octave layer distribution of the interest points. The high variability implies that balancing the processing load among the sensor nodes is a very challenging task, and obtaining a priori information is essential, e.g., through prediction. Our results show that if a priori information is available about the images, then Top-$M$ interest point selection, limited , octave-based processing at the camera node, together with area-based interest point detection and extraction at the processing nodes, can balance the processing load and limit the transmission cost in the network. Complete interest point detection at the camera node with optimized descriptor extraction delegation to the processing nodes in turn can further decrease the transmission load and allow a better balance of the processing load among the network nodes.

  • 3.
    Khan, Muhammad Altamash
    et al.
    KTH, School of Electrical Engineering (EES), Communication Networks. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Dán, György
    KTH, School of Electrical Engineering (EES), Communication Networks. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Fodor, Viktoria
    KTH, School of Electrical Engineering (EES), Communication Networks. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Characterization of SURF Interest Point Distribution for Visual Processing in Sensor Networks2013In: 2013 18th International Conference on Digital Signal Processing, DSP 2013, 2013Conference paper (Refereed)
    Abstract [en]

    We study the statistical characteristics of SURF interest points and descriptors, with the aim of supporting the design of distributed processing across sensor nodes in a resource constrained visual sensor network. We consider a sensor network with a single camera node and four schemes of delegating processing tasks to the sensor nodes. We discuss the potential and the challenges of the different schemes in light of the results of the statistical analysis. Our results show that the distribution of the number of interest points per image exhibits a heavy tail. The interest point locations are almost uniformly distributed along the axes of the images, but their X and Y coordinates are slightly correlated. Most interest points are found in the lowest octave layers, and the number of interest points decreases exponentially with scale. Our analysis suggests that for a wireless broadcast channel delegating subareas of images to processing nodes would lead to a more even allocation than delegating by octave layers. For directional wireless channels the efficiency can be significantly improved by performing some of the feature extraction tasks at the camera node.

  • 4.
    Mårtensson, Jonas
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Alam, Assad
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Behere, Sagar
    KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Mechatronics.
    Khan, Altamash
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Kjellberg, Joakim
    Liang, Kuo-Yun
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Pettersson, Henrik
    Sundman, Dennis
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    The development of a cooperative heavy-duty vehicle for the GCDC 2011: Team Scoop2012In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 13, no 3, 1033-1049 p.Article in journal (Refereed)
    Abstract [en]

    The first edition of the Grand Cooperative Driving Challenge (GCDC) was held in the Netherlands in May 2011. Nine international teams competed in urban and highway platooning scenarios with prototype vehicles using cooperative adaptive cruise control. Team Scoop, a collaboration between KTH Royal Institute of Technology, Stockholm, Sweden, and Scania CV AB, Sodertalje, Sweden, participated at the GCDC with a Scania R-series tractor unit. This paper describes the development and design of Team Scoop's prototype system for the GCDC. In particular, we present considerations with regard to the system architecture, state estimation and sensor fusion, and the design and implementation of control algorithms, as well as implementation issues with regard to the wireless communication. The purpose of the paper is to give a broad overview of the different components that are needed to develop a cooperative driving system: from architectural design, workflow, and functional requirement descriptions to the specific implementation of algorithms for state estimation and control. The approach is more pragmatic than scientific; it collects a number of existing technologies and gives an implementation-oriented view of a cooperative vehicle. The main conclusion is that it is possible, with a modest effort, to design and implement a system that can function well in cooperation with other vehicles in realistic traffic scenarios.

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