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  • 1.
    Annell, Stefan
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Mechatronics.
    Gratner, Alexander
    KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Mechatronics.
    Svensson, Lars
    KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Mechatronics.
    Probabilistic collision estimation system for autonomous vehicles2016In: IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, IEEE conference proceedings, 2016, p. 473-478Conference paper (Refereed)
    Abstract [en]

    Nearly 1.3 million people die each year in traffic-related accidents, whereas an additional 20-50 million peopleare injured. Introducing autonomous vehicles would aim to reduce these numbers by removing the driver from the loop entirely, and thus removing the human error. Intersections are considered a complex traffic situation for autonomous vehicles. Functions which could accurately foresee future events in those situations, mimicking the situation awareness of humans, would improve autonomous systems and increase traffic safety. To address this a system is designed with two main functionalities: estimate the movements of observed vehicles in a general traffic situation and predict the probability of a collision, given the current ego trajectory. This system could either be used as information and feedback for a trajectoryplanner or as support for decision making at higher level system  monitoring. The main contributions are the robust system design, that robustly and consistently estimates the likelihood of a collision and thus preventing future collision, and the intention estimation which determines the probability of which route through an intersection an observed vehicle will take through an intersection by using its current state. The system is validated by controlling the ego vehicle’s velocity with a Velocity Planning Controller to avoid colliding. It is shown that in terms of robustness to noise the system successfully avoids collision.

  • 2.
    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.

  • 3.
    Pereira, Gonçalo Collares
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Centres, Integrated Transport Research Lab, ITRL. KTH, School of Electrical Engineering (EES), Automatic Control.
    Svensson, Lars
    KTH, School of Industrial Engineering and Management (ITM), Centres, Integrated Transport Research Lab, ITRL. KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.).
    Lima, Pedro
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Industrial Engineering and Management (ITM), Centres, Integrated Transport Research Lab, ITRL. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Mårtensson, Jonas
    KTH, School of Industrial Engineering and Management (ITM), Centres, Integrated Transport Research Lab, ITRL. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. KTH, School of Electrical Engineering (EES), Automatic Control.
    Lateral Model Predictive Control for Over-Actuated Autonomous Vehicle2017In: 2017 IEEE Intelligent Vehicles Symposium (IV), Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 310-316, article id 7995737Conference paper (Refereed)
    Abstract [en]

    In this paper, a lateral controller is proposed for an over-Actuated vehicle. The controller is formulated as a linear time-varying model predictive controller. The aim of the controller is to track a desired path smoothly, by making use of the vehicle crabbing capability (sideways movement) and minimizing the magnitude of curvature used. To do this, not only the error to the path is minimized, but also the error to the desired orientation and the control signals requests. The controller uses an extended kinematic model that takes into consideration the vehicle crabbing capability and is able to track not only kinematically feasible paths, but also plan and track over non-feasible discontinuous paths. Ackermann steering geometry is used to transform the control requests, curvature, and crabbing angle, to wheel angles. Finally, the controller performance is evaluated first by simulation and, after, by means of experimental tests on an over-Actuated autonomous research vehicle.

  • 4.
    Svensson, Lars
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Embedded Control Systems.
    Masson, Lola
    Mohan, Naveen
    KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Embedded Control Systems.
    Ward, Erik
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, perception and learning, RPL.
    Pernestål Brenden, Anna
    KTH, School of Industrial Engineering and Management (ITM), Centres, Integrated Transport Research Lab, ITRL.
    Feng, Lei
    KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Embedded Control Systems.
    Törngren, Martin
    KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Embedded Control Systems.
    Safe Stop Trajectory Planning for Highly Automated Vehicles:An Optimal Control Problem Formulation2018In: 2018 IEEE Intelligent Vehicles Symposium (IV), Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 517-522, article id 8500536Conference paper (Refereed)
    Abstract [en]

    Highly automated road vehicles need the capabilityof stopping safely in a situation that disrupts continued normaloperation, e.g. due to internal system faults. Motion planningfor safe stop differs from nominal motion planning, since thereis not a specific goal location. Rather, the desired behavior isthat the vehicle should reach a stopped state, preferably outsideof active lanes. Also, the functionality to stop safely needs tobe of high integrity. The first contribution of this paper isto formulate the safe stop problem as a benchmark optimalcontrol problem, which can be solved by dynamic programming.However, this solution method cannot be used in real-time. Thesecond contribution is to develop a real-time safe stop trajectoryplanning algorithm, based on selection from a precomputedset of trajectories. By exploiting the particular properties ofthe safe stop problem, the cardinality of the set is decreased,making the algorithm computationally efficient. Furthermore, amonitoring based architecture concept is proposed, that ensuresdependability of the safe stop function. Finally, a proof of conceptsimulation using the proposed architecture and the safe stoptrajectory planner is presented.

  • 5.
    Törngren, Martin
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Mechatronics.
    Zhang, Xinhai
    KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Embedded Control Systems.
    Mohan, Naveen
    KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Mechatronics.
    Becker, Matthias
    KTH, School of Electrical Engineering and Computer Science (EECS), Electronics, Electronic and embedded systems.
    Svensson, Lars
    KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Mechatronics.
    Tao, Xin
    KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Embedded Control Systems.
    Chen, DeJiu
    KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Machine Design (Div.). KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Embedded Control Systems. KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Mechatronics.
    Westman, Jonas
    KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Mechatronics. KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Embedded Control Systems.
    Architecting Safety Supervisors for High Levels of Automated Driving2018In: Proceeding of the 21st IEEE Int. Conf. on Intelligent Transportation Systems, IEEE, 2018Conference paper (Refereed)
    Abstract [en]

    The complexity of automated driving poses challenges for providing safety assurance. Focusing on the architecting of an Autonomous Driving Intelligence (ADI), i.e. the computational intelligence, sensors and communication needed for high levels of automated driving, we investigate so called safety supervisors that complement the nominal functionality. We present a problem formulation and a functional architecture of a fault-tolerant ADI that encompasses a nominal and a safety supervisor channel. We then discuss the sources of hazardous events, the division of responsibilities among the channels, and when the supervisor should take over. We conclude with identified directions for further work.

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