Car-following (CF) modeling, an essential component in simulating human CF behaviors, has attracted increasing research interest in the past decades. This paper pushes the state of the art by proposing a novel generative hybrid CF model, which achieves high accuracy in characterizing dynamic human CF behaviors and is able to generate realistic human CF behaviors for any given observed or even unobserved driving style. Specifically, the ability of accurately capturing human CF behaviors is ensured by designing and calibrating an Intelligent Driver Model (IDM) with time-varying parameters. The reason behind is that such time-varying parameters can express both the inter-driver heterogeneity, i.e., diverse driving styles of different drivers, and the intra-driver heterogeneity, i.e., changing driving styles of the same driver. The ability of generating realistic human CF behaviors of any given observed driving style is achieved by applying a neural process (NP) based model. The ability of inferring CF behaviors of unobserved driving styles is supported by exploring the relationship between the calibrated time-varying IDM parameters and an intermediate variable of NP. To demonstrate the effectiveness of our proposed models, we conduct extensive experiments and comparisons, including CF model parameter calibration, CF behavior prediction, and trajectory simulation for different driving styles.
翻译:汽车跟踪模型(CF)是模拟人类CF行为的基本组成部分,在过去几十年中,它吸引了越来越多的研究兴趣。本文通过提出一个新的基因化混合CF模型来推动艺术状态,该模型在动态人类CF行为特征的特征方面具有很高的准确性,能够为任何观察到的、甚至没有观察到的驾驶风格产生现实的人类CF行为。具体地说,通过设计和校准具有时间变化参数的智能驱动模型(IDM),确保了准确捕捉人类CF行为的能力。其背后的原因是,这种时间变化参数既能体现河流间异性混合CF模式,即不同驱动者的不同驾驶风格,又能反映河内异性CFCF行为,即能为任何观察到的驾驶风格带来现实的人类CFC行为。通过应用以神经过程为基础的模型(NP),可以确保准确捕捉到人类CFC行为的能力。这种时间变异的参数能够反映未经观测的驱动风格,即不同驱动者的不同驾驶风格,即不同驾驶者的不同驾驶风格的驱动力模型之间的轨迹,通过探索我们所拟的轨迹变的轨迹模型,以及我们所拟的轨迹变的轨迹变的轨迹模型,来证明。