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

A merging interaction model explains human drivers' behaviour from input signals to decisions