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彻底改变交通运输，但目前它们无法确保安全且高效的驾驶风格。可靠的预测人类行为的模型对于克服这一问题至关重要。虽然数据驱动模型通常用于此目的，但它们在安全关键边缘情况下可能较为脆弱。这促使人们对结合认知理论的模型产生了兴趣，但由于此类模型通常是为解释目的而开发的，因此这种方法在行为预测中的有效性至今尚未得到充分检验。在本文中，我们研究了新型认知合理模型《Commotions》——该模型融合了人类感知、决策和运动控制的最新理论——在间隙接受场景中预测人类行为的实用性，这些场景涉及许多重要的交通交互，如车道变更和交叉路口。我们证明，该模型在多个自然数据集上与成熟的数据驱动预测模型相比具有竞争力，甚至表现更优。这些结果展示了将认知理论融入自动驾驶汽车行为预测模型的前景。