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Grid-Based Stochastic Model Predictive Control for Trajectory Planning in Uncertain Environments

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Document pages: 8 pages

Abstract: Stochastic Model Predictive Control has proved to be an efficient method toplan trajectories in uncertain environments, e.g., for autonomous vehicles.Chance constraints ensure that the probability of collision is bounded by apredefined risk parameter. However, considering chance constraints in anoptimization problem can be challenging and computationally demanding. In thispaper, we present a grid-based Stochastic Model Predictive Control approach.This approach allows to determine a simple deterministic reformulation of thechance constraints and reduces the computational effort, while considering thestochastic nature of the environment. Within the proposed method, we firstdivide the environment into a grid and, for each predicted step, assign eachcell a probability value, which represents the probability that this cell willbe occupied by surrounding vehicles. Then, the probabilistic grid istransformed into a binary grid of admissible and inadmissible cells by applyinga threshold, representing a risk parameter. Only cells with an occupancyprobability lower than the threshold are admissible for the controlled vehicle.Given the admissible cells, a convex hull is generated, which can then be usedfor trajectory planning. Simulations of an autonomous driving highway scenarioshow the benefits of the proposed grid-based Stochastic Model PredictiveControl method.

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