One of the difficult tasks associated with driving in suburban or urban roads is the interactions with pedestrians. One often finds it hard to judge the chances of a pedestrian or bicycle suddenly turning onto the driving path. These leads to a natural slow down response by the drivers. Since these responses are based on the risk feeling of driver, they are probabilistic in nature. This study shares a scenario-based model predictive control algorithm considering probabilistic constraint (SMPC-P) to handle such pedestrian interactions. An Interacting Multiple-Model Kalman Filter (IMM-KF) is used to predict the pedestrian path as multiple trajectories of independent probabilities. The task is formulated into a nonlinear MPC problem. We use a non-linear optimization solver named Interior Point OPTimizer(IPOPT). We introduce a modified form of inverse square root unit function to represent the collision probability into a deterministic function that is compatible with IPOPT. Having simulated it in MATLAB, the controller gives a very natural control behaviour for shared road driving compared to single scenario deterministic MPC.


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    Title :

    Scenario-based model predictive speed controller considering probabilistic constraint for driving scene with pedestrian


    Contributors:


    Publication date :

    2020-09-20


    Size :

    1079867 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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