Humans are experts in making decisions for challenging driving tasks with uncertainties. Many efforts have been made to model the decision-making process of human drivers at the behavior level. However, limited studies explain how human drivers actively make reliable sequential decisions to complete interactive driving tasks in an uncertain environment. This paper argues that human drivers intently search for actions to reduce the uncertainty of their perception of the environment, i.e., perceptual uncertainty, to a low level that allows them to make a trustworthy decision easily. This paper provides a proof of concept framework to empirically reveal that human drivers' perceptual uncertainty decreases when executing interactive tasks with uncertainties. We first introduce an explainable-artificial intelligence approach (i.e., SHapley Additive exPlanation, SHAP) to determine the salient features on which human drivers make decisions. Then, we use entropy-based measures to quantify the drivers' perceptual changes in these ranked salient features across the decision-making process, reflecting the changes in uncertainties. The validation and verification of our proposed method are conducted in the highway on-ramp merging scenario with congested traffic using the INTERACTION dataset. Experimental results support that human drivers intentionally seek information to reduce their perceptual uncertainties in the number and rank of salient features of their perception of environments to make a trustworthy decision.
翻译:人类是针对具有不确定性的驾驶任务做出决策的专家。许多努力在行为层面模拟人类驾驶员的决策过程,但研究有限,解释人类驾驶员如何积极作出可靠的连续决策,以便在不确定的环境中完成交互式驾驶任务。本文认为,人类驾驶员有意寻找行动,以减少其对环境看法的不确定性,即概念上的不确定性,降低到低水平,使其能够轻易作出值得信赖的决定。本文件提供了概念框架的证明,从经验上揭示,在执行具有不确定性的互动任务时,人类驾驶员的观念上的不确定性会减少。我们首先采用了可解释的人工智能方法(即Shanapley Additive Explangation, SHAP),以确定人类驾驶员在不确定的环境中做出决定的特征。然后,我们使用基于诱导的计量措施来量化驾驶员在决策过程中这些显著特征的观念变化,以反映不确定性的变化。我们拟议方法的验证和核查工作是在使用内部认知性数据定位的精确度的模型上进行的。实验结果支持在选择性驱动力的精确度的模型中减少其真实性环境。