A Generative Car-following Model Conditioned On Driving Styles
- URL: http://arxiv.org/abs/2112.05399v1
- Date: Fri, 10 Dec 2021 09:04:13 GMT
- Title: A Generative Car-following Model Conditioned On Driving Styles
- Authors: Yifan Zhang, Xinhong Chen, Jianping Wang, Zuduo Zheng, Kui Wu
- Abstract summary: Car-following (CF) modeling has attracted increasing research interest in the past decades.
This paper proposes a novel generative hybrid CF model, which achieves high accuracy in characterizing dynamic human CF behaviors.
- Score: 13.323897222376756
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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