用于意图感知车辆的仿射模型辨识的最优输入设计
Optimal Input Design for Affine Model Discrimination with Applications in Intention-Aware Vehicles
AI总结：
本文研究了在有限数量的仿射模型中通过最优输入信号设计进行辨识的问题，旨在通过优化分离/辨识（受控）输入，确保所有仿射模型的输出轨迹在初始条件不确定、未受控输入以及过程和测量噪声存在的情况下能够被区分，同时提出了一种新的方法来解决这个问题，强调了模型辨识的保证和最优性，与之前使用鲁棒优化的保守方法形成对比。
摘要：
本文考虑了在存在未受控输入和噪声的情况下，对有限数量的仿射模型进行辨识的最优输入信号设计问题。每个仿射模型代表不同的系统运行模式，对应于其他驾驶员或机器人的未观测意图，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/辨识（受控）输入，使得所有仿射模型的输出轨迹能够相互区分，尽管存在初始条件的不确定性、未受控输入以及过程和测量噪声。我们提出了一种新的方法来解决这个问题，强调模型辨识的保证和最优性，与之前提出的使用鲁棒优化的保守方法形成对比。该新方法可以重新表述为双层优化问题，并进一步改写为混合整数线性规划（MILP）。此外，我们相对通用的问题设定允许在理性代理之间纳入目标和/或责任。例如，每个驾驶员必须遵守交通规则，同时优化安全、舒适和能源效率。最后，我们展示了我们的方法在多个驾驶场景中识别其他车辆意图的有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
