智能驾驶员模型的贝叶斯校准
Bayesian Calibration of the Intelligent Driver Model
AI总结：
提出记忆增强的贝叶斯校准技术并开发MA-IDM模型，通过高斯过程建模时间相关误差，在HighD数据集上实现了比贝叶斯IDM更真实的驾驶行为和更优的不确定性量化。
摘要：
跟驰模型的准确校准对于理解人类驾驶行为和实施高保真微观仿真至关重要。本文提出了一种记忆增强的贝叶斯校准技术，以捕捉模型参数中的不确定性，以及模型预测与观测数据之间时间相关的行为差异。具体而言，我们使用分层贝叶斯框架来表征参数不确定性，并使用高斯过程对时间相关的误差进行建模。我们将该贝叶斯校准技术应用于智能驾驶员模型（IDM），并开发了一种新颖的随机跟驰模型，即记忆增强IDM（MA-IDM）。为了评估MA-IDM的有效性，我们将提出的MA-IDM与假设误差为独立同分布（i.i.d.）的贝叶斯IDM进行比较，基于HighD数据集的仿真结果表明，MA-IDM能够生成更真实的驾驶行为，并提供比贝叶斯IDM更好的不确定性量化。通过分析高斯过程的长度尺度参数，我们还表明，考虑过去驾驶行为有助于改善跟驰模型的校准。
英文摘要：
Accurate calibration of car-following models is essential for understanding human driving behaviors and implementing high-fidelity microscopic simulations. This work proposes a memory-augmented Bayesian calibration technique to capture both uncertainty in the model parameters and the temporally correlated behavior discrepancy between model predictions and observed data. Specifically, we characterize the parameter uncertainty using a hierarchical Bayesian framework and model the temporally correlated errors using Gaussian processes. We apply the Bayesian calibration technique to the intelligent driver model (IDM) and develop a novel stochastic car-following model named memory-augmented IDM (MA-IDM). To evaluate the effectiveness of MA-IDM, we compare the proposed MA-IDM with Bayesian IDM in which errors are assumed to be i.i.d., and our simulation results based on the HighD dataset show that MA-IDM can generate more realistic driving behaviors and provide better uncertainty quantification than Bayesian IDM. By analyzing the lengthscale parameter of the Gaussian process, we also show that taking the driving actions from the past five seconds into account can be helpful in modeling and simulating the human driver's car-following behaviors.
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。
