The development of automated vehicles has the potential to revolutionize transportation, but they are currently unable to ensure a safe and time-efficient driving style. Reliable models predicting human behavior are essential for overcoming this issue. While data-driven models are commonly used to this end, they can be vulnerable in safety-critical edge cases. This has led to an interest in models incorporating cognitive theory, but as such models are commonly developed for explanatory purposes, this approach's effectiveness in behavior prediction has remained largely untested so far. In this article, we investigate the usefulness of the \emph{Commotions} model -- a novel cognitively plausible model incorporating the latest theories of human perception, decision-making, and motor control -- for predicting human behavior in gap acceptance scenarios, which entail many important traffic interactions such as lane changes and intersections. We show that this model can compete with or even outperform well-established data-driven prediction models across several naturalistic datasets. These results demonstrate the promise of incorporating cognitive theory in behavior prediction models for automated vehicles.

翻译：自动驾驶汽车的发展有望革新交通运输，但目前它们仍无法确保安全且高效的驾驶风格。为解决此问题，可靠的人类行为预测模型至关重要。尽管数据驱动模型常被用于此目的，但在安全关键边缘场景中可能较为脆弱。这引发了人们对融入认知理论的模型的兴趣，然而此类模型通常为解释目的而开发，其在行为预测中的有效性迄今尚未得到充分验证。本文研究了“Commotions”模型——一种结合人类感知、决策和运动控制最新理论的新型认知合理模型——在间隙接受场景中预测人类行为的实用性，此类场景包含车道变更和交叉路口等许多重要交通交互。我们表明，该模型在多个自然场景数据集上的表现可与既定数据驱动预测模型竞争甚至更优。这些结果证明了将认知理论融入自动驾驶汽车行为预测模型的潜力。