论文标题
智能驾驶员模型的贝叶斯校准
Bayesian Calibration of the Intelligent Driver Model
论文作者
论文摘要
对汽车跟随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模拟至关重要。这项工作提出了一种记忆增强的贝叶斯校准技术，以捕获模型参数中的不确定性以及模型预测和观察到的数据之间的时间相关行为差异。具体而言，我们使用层次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。我们将贝叶斯校准技术应用于智能驱动器模型（IDM），并开发出一种名为Memory-agentimagment IDM（MA-IDM）的新型随机汽车跟随模型。为了评估MA-IDM的有效性，我们将所提出的MA-IDM与贝叶斯IDM进行比较，其中假定错误是I.I.D.，并且基于HighD数据集的模拟结果表明，MA-IDM可以产生更真实的驾驶行为，并比Bayesian IDM更好地提供更好的不确定性定量。通过分析高斯流程的长度尺度参数，我们还表明，从过去五秒钟中考虑驾驶动作可能有助于建模和模拟人类驾驶员的跟踪行为。
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.
