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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.
    Oliveira, Rui
    KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.
    Motion Planning for Heavy-Duty Vehicles2019Licentiate thesis, monograph (Other academic)
    Abstract [en]

    Autonomous driving is a disrupting technology that is expected to reshape transportation systems. The benefits of autonomous vehicles include, but are not limited to, safer transportation, increased economic growth, and broader access to mobility services. Industry and academia are currently researching a variety of topics related to autonomous driving, however, the focus seems to be on passenger vehicles. As a consequence, heavy-duty vehicles, which are a significant share of transportation systems, are overlooked, and the challenges associated with these vehicles are neglected.

    This thesis studies motion planning algorithms for heavy-duty vehicles. Motion planning is a fundamental part of autonomous vehicles, it is tasked with finding the correct sequence of actions that take the vehicle towards its goal. This work focuses on particular aspects that distinguish heavy-duty vehicles from passenger vehicles, and that call for novel developments within motion planning algorithms.

    We start by addressing the problem of finding shortest paths for a vehicle in obstacle-free environments. This problem has been studied since the fifties, but the addressed vehicle models are often simplistic. We propose a novel algorithm that is able to plan paths respecting complex vehicle actuator constraints associated with the slow dynamics of heavy vehicles.

    Using the previous method, we tackle the motion planning problem in environments populated with obstacles. Lattice-based motion planners, a popular choice for this type of scenario, come with drawbacks related to the sub-optimality of solution paths, and the discretization of the goal state. We propose a novel path optimization method, which is able to significantly reduce both problems. The resulting optimized paths contain less oscillatory behavior and arrive precisely at arbitrary non-discretized goal states.

    We then study the problem of bus driving in urban environments. It is shown how this type of driving is fundamentally different than that of other vehicles, due to the chassis configuration with large overhangs. To successfully maneuver buses, distinct driving objectives need to be used in planning algorithms. Moreover, a novel environment classification scheme must be introduced. The result is a motion planning algorithm that is able to mimic professional bus driver behavior, resulting in safer driving and increased vehicle maneuverability.

     

  • 3.
    Oliveira, Rui Filipe De Sousa
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.
    Lima, Pedro F.
    KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.
    Cirillo, M.
    Mårtensson, Jonas
    KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.
    Wahlberg, Bo
    KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Optimization and Systems Theory.
    Trajectory Generation using Sharpness Continuous Dubins-like Paths with Applications in Control of Heavy-Duty Vehicles2018In: 2018 European Control Conference, ECC 2018, Institute of Electrical and Electronics Engineers Inc. , 2018, p. 935-940Conference paper (Refereed)
    Abstract [en]

    We present a trajectory generation framework for control of wheeled vehicles under steering actuator constraints. The motivation is smooth driving of autonomous heavy-duty vehicles, which are characterized by slow actuator dynamics. In order to deal with the slow dynamics, we take into account rate and, additionally, torque limitations of the steering actuator directly. Previous methods only take into account limitations in the path curvature, which deals indirectly with steering rate limitations. We propose the new concept of Sharpness Continuous curves, which uses cubic curvature paths together with circular arcs to steer the vehicle. The obtained paths are characterized by a smooth and continuously differentiable steering angle profile. The final trajectories computed with our method provide low-level controllers with reference signals which are easier to track, resulting in improved performance. The smoothness of the obtained steering profiles also results in increased passenger comfort. The method is characterized by fast computation times. We detail possible path planning applications of the method, and conduct simulations that show its advantages and real-time capabilities.

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