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  • 1.
    Gao, Yulong
    et al.
    Department of Computer Science, University of Oxford, Oxford, UK.
    Yan, Shuhao
    School of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA.
    Zhou, Jian
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, Faculty of Science & Engineering.
    Cannon, Mark
    Department of Engineering Science, University of Oxford, UK.
    Abate, Alessandro
    Department of Computer Science, University of Oxford, Oxford, UK.
    Johansson, Karl H.
    Division of Decision and Control Systems, KTH Royal Institute of Technology, Stockholm, Sweden; Digital Futures, Stockholm, Sweden.
    Robust Tube Model Predictive Control with Uncertainty Quantification for Discrete-Time Linear SystemsManuscript (preprint) (Other academic)
    Abstract [en]

    This paper is concerned with model predictive control (MPC) of discrete-time linear systems subject to bounded additive disturbance and hard constraints on the state and input, whereas the true disturbance set is unknown. Unlike most existing work on robust MPC, we propose an MPC algorithm incorporating online uncertainty quantification that builds on prior knowledge of the disturbance, i.e., a known but conservative disturbance set. We approximate the true disturbance set at each time step with a parameterised set, which is referred to as a quantified disturbance set, using the scenario approach with additional disturbance realisations collected online. A key novelty of this paper is that the parameterisation of these quantified disturbance sets enjoy desirable properties such that the quantified disturbance set and its corresponding rigid tube bounding disturbance propagation can be efficiently updated online. We provide statistical gaps between the true and quantified disturbance sets, based on which, probabilistic recursive feasibility of MPC optimisation problems are discussed. Numerical simulations are provided to demonstrate the efficacy of our proposed algorithm and compare with conventional robust MPC algorithms.

  • 2.
    Zhou, Jian
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, Faculty of Science & Engineering.
    Interaction and Uncertainty-Aware Motion Planning for Autonomous Vehicles Using Model Predictive Control2023Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    Motion planning plays a significant role in enabling advances of autonomous vehicles in saving lives and improving traffic efficiency. In a predictive motion-planning strategy, the ego vehicle predicts the motion of surrounding vehicles and uses these predictions to plan collision-free reference trajectories. In dynamic multi-vehicle traffic environments, a key research question is how to consider vehicle-to-vehicle interactions and motion uncertainties of the surrounding vehicles in the motion planner to achieve resilient motion planning of the autonomous ego vehicle. 

    This Licentiate Thesis proposes a model predictive control (MPC)-based approach to achieve safe motion planning in uncertain and dynamic multi-vehicle driving environments. MPC has been widely applied for the motion planning of autonomous vehicles. However, designing resilient MPC-based motion planners that consider interactions and uncertainties of surrounding vehicles remains an open and challenging problem, which is the primary motivation for the research presented in this thesis. 

    This thesis makes several contributions toward solving the interaction and uncertainty-aware motion-planning problems. The first contribution is an MPC, which is called interaction-aware moving target MPC. It is designed based on the combination of an interaction-aware motion-prediction model and time-varying reference targets of the optimal control problem for proactive and non-local trajectory planning in multi-vehicle dynamic scenarios. 

    In the second contribution, the proposed MPC is extended to account for the multi-modal motion uncertainties of surrounding vehicles, including the maneuver and trajectory uncertainties, which are predicted by combining an interaction-aware motion-prediction model and a data-driven approach. Based on the modeling of uncertainties, a safety-awareness parameter is included in the design to compute the obstacle occupancy for achieving a trade-off between the performance and robustness of the MPC planner. The efficiency of the method is illustrated in challenging highway-driving simulation scenarios and a driving scenario from a recorded traffic dataset. 

    The third contribution of this thesis is quantifying the motion uncertainty of surrounding obstacles to reduce the conservativeness of the motion planner while pursuing robustness. To this end, a robust motion-planning method is designed for robotic systems based on uncertainty quantification of surrounding obstacles. The proposed MPC is called risk-aware robust MPC, as the risk of robustness reduction through uncertainty quantification is analyzed. Simulations in highway merging scenarios of an autonomous vehicle with uncertain surrounding vehicles show that the approach is less conservative than a conventional robust MPC and more robust than a deterministic MPC.  

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  • 3.
    Zhou, Jian
    et al.
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, Faculty of Science & Engineering.
    Olofsson, Björn
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, Faculty of Science & Engineering. Department of Automatic Control, Lund University, Lund, Sweden.
    Frisk, Erik
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, Faculty of Science & Engineering.
    Interaction-Aware Motion Planning for Autonomous Vehicles with Multi-Modal Obstacle Uncertainties Using Model Predictive ControlManuscript (preprint) (Other academic)
    Abstract [en]

    This paper proposes an interaction-aware motion-planning method for an autonomous vehicle in uncertain multi-vehicle traffic environments. An interaction-aware motion-prediction model is used to predict the behaviors of surrounding vehicles. The multi-modal prediction uncertainties, containing both the maneuver and trajectory uncertainties of surrounding vehicles, are considered in the method for resilient motion planning of the ego vehicle. Based on the prediction of the surrounding vehicles, an optimal reference trajectory of the ego vehicle is computed by model predictive control (MPC) to follow the time-varying reference targets and avoid collisions with obstacles. A trade-off between the performance and robustness of the method can be achieved by tuning a safety-awareness parameter in the MPC. The efficiency of the method is illustrated in challenging highway-driving simulation scenarios and a driving scenario from a recorded traffic dataset.

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