The framework of the CPIDLM model (IMAGE)
Caption
The CPIDLM network architecture is illustrated in Figure. The architecture consists of four modules labeled (a) through (d). (a) The driver heterogeneity and uncertainty feature learning module extracts causal behavioral features based on a causal graph of driver behavior, enabling effective modeling of driver heterogeneity and uncertainty. This module explicitly expresses these variations through five IDM parameters that vary with traffic conditions and time. The parameters output to characterize driver heterogeneity can be directly applied to vehicle platoon dynamics modeling and simulation. (b) The physical module uses these five parameters to compute physical predictions of driver behavior according to the IDM’s computational graph. (c) The data-driven vehicle motion feature learning module builds vehicle following behavior characteristics solely from large-scale vehicle platoon data to predict vehicle trajectories purely from data. (d) The adaptive weight module dynamically outputs gating thresholds based on each vehicle’s state to integrate the physical predictions with those from the data-driven module. The key advantage of this framework is that the predictions are constrained by physical prior formulas, ensuring consistency with physical realities and producing more reliable and stable results.
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Communications in Transportation Research
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