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  • 1.
    Kokogias, Stefanos
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Centres, Integrated Transport Research Lab, ITRL.
    Svensson, Lars
    KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.).
    Pereira, Goncalo Collares
    KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.
    Oliveira, Rui
    KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.
    Zhang, Xinhai
    KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.).
    Song, Xinwu
    KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.).
    Mårtensson, Jonas
    KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.
    Development of Platform-Independent System for Cooperative Automated Driving Evaluated in GCDC 20162018In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 19, no 4, p. 1277-1289Article in journal (Refereed)
    Abstract [en]

    Cooperative automated driving is a promising development in reducing energy consumption and emissions, increasing road safety, and improving traffic flow. The Grand Cooperative Driving Challenge (GCDC) 2016 was an implementation oriented project with the aim to accelerate research and development in the field. This paper describes the development of the two vehicle systems with which KTH participated in GCDC 2016. It presents a reference system architecture for collaborative automated driving as well as its instantiation on two conceptually different vehicles: a Scania truck and the research concept vehicle, built at KTH. We describe the common system architecture, as well as the implementation of a selection of shared and individual system functionalities, such as V2X communication, localization, state estimation, and longitudinal and lateral control. We also present a novel approach to trajectory tracking control for a four-wheel steering vehicle using model predictive control and a novel method for achieving fair data age distribution in vehicular communications.

  • 2.
    Zhang, Xinhai
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.).
    Song, Xinwu
    KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.).
    Feng, Lei
    KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Mechatronics.
    Chen, Lei
    Törngren, Martin
    KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Mechatronics.
    A Case Study on Achieving Fair Data Age Distribution in Vehicular Communications2017In: PROCEEDINGS OF THE 23RD IEEE REAL-TIME AND EMBEDDED TECHNOLOGY AND APPLICATIONS SYMPOSIUM (RTAS 2017) / [ed] Parmer, G, IEEE , 2017, p. 307-317Conference paper (Refereed)
    Abstract [en]

    In vehicular communication protocol stacks, received messages may not always be decoded successfully due to the complexity of the decoding functions, the uncertainty of the communication load and the limited computation resources. Even worse, an improper implementation of the protocol stack may cause an unfair data age distribution among all the communicating vehicles (the receiving bias problem). In such cases, some vehicles are almost locked out of the vehicular communication, causing potential safety risk in scenarios such as intersection passing. To our knowledge, this problem has not been systematically studied in the fields of vehicular communication and intelligent transport systems (ITS). This paper analyzes the root of the receiving bias problem and proposes architectural solutions to balance data age distribution. Simulation studies based on commercial devices demonstrate the effectiveness of these solutions. In addition, our system has been successfully applied during the Grand Cooperative Driving Challenge, where complicated scenarios involving platooning maneuvering and intersection coordination were conducted.

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