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Stochastic model predictive controller with chance constraints for comfortable and safe driving behavior of autonomous vehicles

机译:具有机会约束的随机模型预测控制器,用于自动驾驶汽车的舒适和安全驾驶行为

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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-the-shelf 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.
机译:在本文中,我们解决了带有机会约束的随机模型预测控制在自动驾驶中的应用。我们使用线性化车辆模型的压缩公式来设置具有非线性机会约束的二次程序,可以使用现有的优化算法来求解该程序。我们进一步展示了如何在路径规划阶段将障碍物信息转换为一组线性状态约束,这些约束可以直接在控制算法中使用。最终的控制器具有潜在的实时能力,并在控制行为的安全性和舒适性之间实现了折衷。

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