Driving automation holds significant potential for enhancing traffic safety. However, effectively handling interactions with human drivers in mixed traffic remains a challenging task. Several models exist that attempt to capture human behavior in traffic interactions, often focusing on gap acceptance. However, it is not clear how models of an individual driver's gap acceptance can be translated to dynamic human-AV interactions in the context of high-speed scenarios like overtaking. In this study, we address this issue by employing a cognitive process approach to describe the dynamic interactions by the oncoming vehicle during overtaking maneuvers. Our findings reveal that by incorporating an initial decision-making bias dependent on the initial velocity into existing drift-diffusion models, we can accurately describe the qualitative patterns of overtaking gap acceptance observed previously. Our results demonstrate the potential of the cognitive process approach in modeling human overtaking behavior when the oncoming vehicle is an AV. To this end, this study contributes to the development of effective strategies for ensuring safe and efficient overtaking interactions between human drivers and AVs.

翻译：驾驶自动化在提升交通安全方面具有显著潜力。然而，在混合交通中有效处理与人类驾驶员的交互仍然是一项艰巨任务。现有多种模型试图捕捉交通交互中的人类行为，通常聚焦于间隙接受。但目前尚不清楚个体驾驶员的间隙接受模型如何转化为高速场景（如超车）中动态的人机交互。本研究采用认知过程方法，通过描述超车机动中与被超越车辆之间的动态交互来应对这一挑战。研究发现，通过将依赖于初始速度的初始决策偏差纳入现有漂移扩散模型，能够准确描述先前观测到的超车间隙接受定性模式。我们的结果表明，当被超越车辆为自动驾驶车辆时，认知过程方法在模拟人类超车行为方面具有潜力。由此，本研究为制定确保人类驾驶员与自动驾驶车辆之间安全高效超车交互的有效策略做出了贡献。