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Temporal vs. spatial formulation of autonomous overtaking algorithms

机译:自主超车算法的时间与空间表达

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This paper concerns optimally controlling an autonomous vehicle to perform safe and comfortable overtaking of a slower moving leading vehicle using model predictive control. The contribution of this paper is to further analyze the convex relaxation that was introduced in [1] in order to see how it compares with the standard formulation. The main difference between the formulations is that the sampling is done in the temporal domain in the standard formulation, but in the spatial domain for the other. It is shown that it is easy to convert one formulation to the other. Further, it is shown that the formulations are identical under the assumption of constant longitudinal velocity of the controlled vehicle. However, when the longitudinal velocity is not fixed they are not identical, in fact, the temporal formulation becomes a mixed integer problem which is hard to convexify. On the other hand, the spatial formulation can be easily convexified using standard methods. Therefore, in the case of changing velocity, we have to rely on the spatial formulation to achieve good solutions in short computation time.
机译:本文涉及使用模型预测控制对自动驾驶车辆进行最佳控制,以使其安全,舒适地超越慢速行驶的领先车辆。本文的目的是进一步分析[1]中引入的凸松弛,以了解其与标准公式的比较。公式之间的主要区别在于,采样是在标准公式的时域中进行的,而在另一个方面是在空间域中进行的。结果表明,很容易将一种配方转换为另一种配方。此外,表明在受控车辆的恒定纵向速度的假设下,配方是相同的。然而,当纵向速度不固定时,它们并不相同,实际上,时间公式成为一个混合整数问题,难以凸现。另一方面,可以使用标准方法轻松地凸显空间公式。因此,在速度变化的情况下,我们必须依靠空间公式来在短的计算时间内获得良好的解决方案。

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