One of the bottlenecks of automated driving technologies is safe and socially acceptable interactions with human-driven vehicles, for example during merging. Driver models that provide accurate predictions of joint and individual driver behaviour of high-level decisions, safety margins, and low-level control inputs are required to improve the interactive capabilities of automated driving. Existing driver models typically focus on one of these aspects. Unified models capturing all aspects are missing which hinders understanding of the principles that govern human traffic interactions. This in turn limits the ability of automated vehicles to resolve merging interactions. Here, we present a communication-enabled interaction model based on risk perception with the potential to capture merging interactions on all three levels. Our model accurately describes human behaviour in a simplified merging scenario, addressing both individual actions (such as velocity adjustments) and joint actions (such as the order of merging). Contrary to other interaction models, our model does not assume humans are rational and explicitly accounts for communication between drivers. Our results demonstrate that communication and risk-based decision-making explain observed human interactions on multiple levels. This explanation improves our understanding of the underlying mechanisms of human traffic interactions and poses a step towards interaction-aware automated driving.

翻译：自动化驾驶技术的一个瓶颈是与人类驾驶车辆的安全且社会可接受的交互，例如在汇入场景中。为提升自动驾驶的交互能力，需要能够准确预测驾驶员高层决策、安全裕度以及低层控制输入的共同与个体行为模型。现有驾驶员模型通常仅关注这些方面之一，缺乏能够涵盖所有方面的统一模型，这阻碍了对人类交通交互原理的理解，进而限制了自动驾驶车辆解决汇入交互的能力。本文提出了一种基于风险感知的通信增强型交互模型，该模型具备在三个层面捕捉汇入交互的潜力。我们的模型在简化的汇入场景中准确描述了人类行为，同时涵盖了单个动作（如速度调整）和共同动作（如汇入顺序）。与其他交互模型不同，我们的模型不假设人类理性，并明确考虑了驾驶员之间的通信。研究结果表明，基于通信和风险的决策机制能够多层次解释所观察的人类交互行为。这一解释加深了我们对人类交通交互潜在机制的理解，并为实现交互感知型自动驾驶迈出了重要一步。