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Motion-prediction algorithms for vehicles often employ historical behavior of a vehicle, rely on the Markov property of the underlying system, and predict the future behavior of the vehicle. However, the Markov property alone may lead to conservative predictions and heavy computational burden. To overcome these drawbacks, this paper develops a method that uses the notion of similarity among vehicle...
We present a scenario-decomposition based Alternating Direction Method of Multipliers (ADMM) algorithm for the efficient solution of scenario-based Model Predictive Control (MPC) problems which arise for instance in the control of stochastic systems. We duplicate the variables involved in the non-anticipativity constraints which allows to develop an ADMM algorithm in which the computations scale linearly...
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