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Humans are experts in making decisions for challenging driving tasks withuncertainties. Many efforts have been made to model the decision-making processof human drivers at the behavior level. However, limited studies explain howhuman drivers actively make reliable sequential decisions to completeinteractive driving tasks in an uncertain environment. This paper argues thathuman drivers intently search for actions to reduce the uncertainty of theirperception of the environment, i.e., perceptual uncertainty, to a low levelthat allows them to make a trustworthy decision easily. This paper provides aproof of concept framework to empirically reveal that human drivers' perceptualuncertainty decreases when executing interactive tasks with uncertainties. Wefirst introduce an explainable-artificial intelligence approach (i.e., SHapleyAdditive exPlanation, SHAP) to determine the salient features on which humandrivers make decisions. Then, we use entropy-based measures to quantify thedrivers' perceptual changes in these ranked salient features across thedecision-making process, reflecting the changes in uncertainties. Thevalidation and verification of our proposed method are conducted in the highwayon-ramp merging scenario with congested traffic using the INTERACTION dataset.Experimental results support that human drivers intentionally seek informationto reduce their perceptual uncertainties in the number and rank of salientfeatures of their perception of environments to make a trustworthy decision.

On Trustworthy Decision-Making Process of Human Drivers from the View of Perceptual Uncertainty Reduction