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2018 ACM/IEEE 9th International Conference on Cyber-Physical Systems (ICCPS)最新文献

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Optimal Input Design for Affine Model Discrimination with Applications in Intention-Aware Vehicles 仿射模型识别的最优输入设计及其在意图感知车辆中的应用
Pub Date : 2017-02-03 DOI: 10.1109/ICCPS.2018.00036
Emil Jacobsen, Farshad Harirchi, Sze Zheng Yong, N. Ozay
This paper considers the optimal design of input signals for the purpose of discriminating among a finite number of affine models with uncontrolled inputs and noise. Each affine model represents a different system operating mode, corresponding to unobserved intents of other drivers or robots, or to fault types or attack strategies, etc. The input design problem aims to find optimal separating/discriminating (controlled) inputs such that the output trajectories of all the affine models are guaranteed to be distinguishable from each other, despite uncertainty in the initial condition and uncontrolled inputs as well as the presence of process and measurement noise. We propose a novel formulation to solve this problem, with an emphasis on guarantees for model discrimination and optimality, in contrast to a previously proposed conservative formulation using robust optimization. This new formulation can be recast as a bilevel optimization problem and further reformulated as a mixed-integer linear program (MILP). Moreover, our fairly general problem setting allows the incorporation of objectives and/or responsibilities among rational agents. For instance, each driver has to obey traffic rules, while simultaneously optimizing for safety, comfort and energy efficiency. Finally, we demonstrate the effectiveness of our approach for identifying the intention of other vehicles in several driving scenarios.
本文考虑输入信号的优化设计,以区分有限数量的具有不可控输入和噪声的仿射模型。每个仿射模型代表不同的系统运行模式,对应于其他驱动程序或机器人的未观察到的意图,或故障类型或攻击策略等。输入设计问题旨在找到最优的分离/判别(受控)输入,使所有仿射模型的输出轨迹能够相互区分,尽管初始条件和非受控输入存在不确定性,并且存在过程和测量噪声。我们提出了一个新的公式来解决这个问题,与之前提出的使用鲁棒优化的保守公式相比,它强调了模型判别和最优性的保证。这个新公式可以重新表述为一个双层优化问题,并进一步重新表述为一个混合整数线性规划(MILP)。此外,我们相当普遍的问题设置允许在理性代理之间合并目标和/或责任。例如,每个司机都必须遵守交通规则,同时优化安全性、舒适性和能源效率。最后,我们展示了我们的方法在几个驾驶场景中识别其他车辆意图的有效性。
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引用次数: 16
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2018 ACM/IEEE 9th International Conference on Cyber-Physical Systems (ICCPS)
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