Intervention minimized semi-autonomous control using decoupled model predictive control

Hayoung Kim, Jeongmin Cho, Dongchan Kim, K. Huh
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引用次数: 13

Abstract

This paper proposes semi-autonomous control that minimizes intervention considering driver's steering and braking intentions. The biggest challenge of this problem is how to fairly judge driver's intentions that appear differently in the lateral and longitudinal directions and how to minimize controller intervention. A decoupled model predictive control (MPC) and optimal intervention decision methods are proposed considering driver incompatibility. Several MPCs are designed first considering the fact that the driver can avoid obstacles either by braking or moving to the left or right lanes. The control input to avoid the collision is calculated for each MPC such that its intervention can be minimized reflecting the driver's intention. After driver incompatibility is formalized, the optimal input is selected to minimize the incompatibility among the paths that can avoid accidents. The proposed algorithm is validated in simulations where collision can be avoided while minimizing the intervention.
采用解耦模型预测控制的干预最小化半自主控制
本文提出了一种考虑驾驶员转向和制动意图的最小化干预的半自主控制。这个问题的最大挑战是如何公平地判断驾驶员在横向和纵向上的不同意图,以及如何最大限度地减少控制器的干预。提出了一种考虑驱动不兼容的解耦模型预测控制(MPC)和最优干预决策方法。一些mpc的设计首先考虑到驾驶员可以通过刹车或移动到左车道或右车道来避开障碍物。对每个MPC计算避免碰撞的控制输入,使其干预可以最小化,以反映驾驶员的意图。将驱动程序不兼容形式化后,选择最优输入,使路径之间的不兼容最小化,从而避免事故的发生。仿真结果表明,该算法在避免碰撞的同时能最大限度地减少干扰。
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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