Human drivers use nonverbal communication and anticipation of other drivers' actions to master conflicts occurring in everyday driving situations. Without a high penetration of vehicle-to-vehicle communication an autonomous vehicle has to have the possibility to understand intentions of others and share own intentions with the surrounding traffic participants. This paper proposes a cooperative combinatorial motion planning algorithm without the need for inter vehicle communication based on Monte Carlo Tree Search (MCTS). We motivate why MCTS is particularly suited for the autonomous driving domain. Furthermore, adoptions to the MCTS algorithm are presented as for example simultaneous decisions, the usage of the Intelligent Driver Model as microscopic traffic simulation, and a cooperative cost function. We further show simulation results of merging scenarios in highway-like situations to underline the cooperative nature of the approach.
In this paper, we address the application of stochastic model predictive control with chance constraints to autonomous driving. We use a condensed formulation of a linearized vehicle model to setup a quadratic program with nonlinear chance constraints, which can be solved with off-theshelf optimization algorithms. We further show how obstacle information in the path planning stage can be converted into a set of linear state constraints that can be directly used in the control algorithm. The resulting controller is potentially real-time capable and achieves a tradeoff between safety and comfort in its control behavior.
Surrogate-based optimization of distillation columns using an iterative Kriging approach is investigated. To avoid suboptimal local minima the focus lies on deterministic global optimization. The determination of optimal setups and operating conditions for ideal and non-ideal distillation columns, leading to mixed-integer nonlinear programming (MINLP) problems, serve as case studies. To cope with output multiplicities of the model an implicit surrogate formulation is proposed.
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