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

David Lenz, Tobias Kessler, A. Knoll
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引用次数: 29

Abstract

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.
基于机会约束的自动驾驶汽车舒适安全驾驶随机模型预测控制器
本文研究了随机模型预测控制在自动驾驶中的应用。我们使用一个线性化车辆模型的简化公式来建立一个具有非线性机会约束的二次规划,该规划可以用现成的优化算法求解。我们进一步展示了如何将路径规划阶段的障碍物信息转换为一组可以直接用于控制算法的线性状态约束。由此产生的控制器具有潜在的实时性,并且在其控制行为中实现了安全性和舒适性之间的权衡。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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