动力学匹配物理储备池计算用于欠感知交通预测
Dynamics-matched Physical Reservoir Computing for Undersensed Traffic Prediction
AI总结：
本研究提出将交通网络作为储备池的IIDM-RC方法，用于欠感知交通预测，在预测准确性和训练时间上优于ESNs与LSTM网络。
AI中文摘要：
机器学习方法越来越多地应用于自动驾驶等场景的交通预测，这类预测必须兼具高准确性与即时可用性，因此低计算成本、快训练时间的方法备受关注。储备池计算便是其中一种，其利用非线性系统的丰富动力学作为计算基底，仅需训练线性读出向量。本研究将交通网络作为储备池，用于预测欠感知交通网络的行为，这种高度非线性动力学的匹配使储备池与目标网络的行为间能实现相似编码，从而支持更直接的预测。研究表明，由改进智能驾驶员模型（Improved Intelligent Driver Model, IIDM）控制的储备池满足一类慢时变输入的回声状态特性；通过仿真验证，该回声状态特性可能适用于更广泛的输入类别，且IIDM储备池计算机（IIDM-RC）能准确预测由跟车模型变化的欠感知车辆网络。此外，研究还与回声状态网络（Echo State Networks, ESNs）和长短期记忆（Long Short-Term Memory, LSTM）网络对比，发现IIDM-RC在预测准确性和训练时间上均有提升。
英文摘要：
Machine learning methods are increasingly used for traffic prediction in applications such as autonomous driving. Such predictions must be both highly accurate and immediately available, making methods with low computational costs and fast training times of interest. One such method is reservoir computing, in which the rich dynamics of a nonlinear system serves as a computational substrate and only a linear readout vector is trained. In this work we use a traffic network as the reservoir for predicting the behavior of an undersensed traffic network. This matching of the highly nonlinear dynamics allows for similar encoding between the behaviors of the reservoir and target network, enabling a more direct prediction. We show that a reservoir governed by the Improved Intelligent Driver Model (IIDM) satisfies the echo state property for a class of slowly-varying inputs. Through simulations we show that the echo state property likely holds for a larger class of inputs, and that the IIDM reservoir computer (IIDM-RC) accurately predicts an undersensed vehicle network governed by varying car-following models. We also compare with echo state networks (ESNs) and Long Short-Term Memory (LSTM) networks, finding improvements using IIDM-RC in both prediction accuracy and training time.
