Understanding how human drivers handle inter-actions with each other can aid the development of automated vehicles capable of operating in mixed traffic. Interactions between human drivers are often complex, so driver behavior models are needed to better understand them. However, existing models mostly focus on the behavior of one driver, which limits their ability to explain complex reciprocal interactions between multiple drivers. At the same time, the prior research that does focus on interactive behaviors of two or more drivers is typically limited to describing drivers' tactical decisions, limiting the understanding of how these decisions are related to operational aspects of behavior (safety margins and control inputs). In this work, we address this gap, focusing specif-ically on highway merging interactions. We build upon the Communication-Enabled Interactions (CEI) framework - a previously proposed holistic approach to interaction modeling. We develop a CEI-based model of highway merging that captures both tactical and operational aspects of the behavior of two drivers interacting in a highway merging scenario. Our model exhibits human-like behavior aligned with empirical observations of high-level decisions (i.e., who goes first?), safety margins (headways), and position and velocity profiles. Based on our model, we identify key mechanisms regarding drivers' beliefs, velocity perception, and planning, which can potentially generalize beyond highway merging to other interactive human driving behaviors. Our findings highlight the potential of the CEI framework in modeling reciprocal traffic interactions in realistic traffic scenarios, and contribute to understanding the complexities of interactions between human drivers.

When two pedestrians approach each other on the sidewalk head-on, they sometimes engage in an awkward interaction, both deviating to the same side (repeatedly) to avoid a collision. This phenomenon is known as the sidewalk salsa. Although well known, no existing model describes how this "dance" arises. Such a model must capture the nuances of individual interactions between pedestrians that lead to the sidewalk salsa. Therefore, it could be helpful in the development of mobile robots that frequently participate in such individual interactions, for example, by informing robots in their decision-making. Here, I present a model based on the communication-enabled interaction framework capable of reproducing the sidewalk salsa. The model assumes pedestrians have a deterministic plan for their future movements and a probabilistic belief about the movements of another pedestrian. Combined, the plan and belief result in a perceived risk that pedestrians try to keep below a personal threshold. In simulations of this model, the sidewalk salsa occurs in a symmetrical scenario. At the same time, it shows behavior comparable to observed real-world pedestrian behavior in scenarios with initial position offsets or risk threshold differences. Two other scenarios provide support for a hypothesis from literature stating that cultural norms –in the form of a biased belief about on which side others will pass (i.e. deviating to the left or right)– contribute to the occurrence of the sidewalk salsa. Thereby, the proposed model provides insight into how the sidewalk salsa arises.

Recently, multiple naturalistic traffic datasets of human-driven trajectories have been published (e.g., highD, NGSim, and pNEUMA). These datasets have been used in studies that investigate variability in human driving behavior, for example for scenario-based validation of autonomous vehicle (AV) behavior, modeling driver behavior, or validating driver models. Thus far, these studies focused on the variability on an operational level (e.g., velocity profiles during a lane change), not on a tactical level (i.e., to change lanes or not). Investigating the variability on both levels is necessary to develop driver models and AV s that include multiple tactical behaviors. To expose multi-level variability, the human responses to the same traffic scene could be investigated. However, no method exists to automatically extract similar scenes from datasets. Here, we present a four-step extraction method that uses the Hausdorff distance, a mathematical distance metric for sets. We performed a case study on the highD dataset that showed that the method is practically applicable. The human responses to the selected scenes exposed the variability on both the tactical and operational levels. With this new method, the variability in operational and tactical human behavior can be investigated, without the need for costly and time-consuming driving-simulator experiments.

A major challenge for autonomous vehicles is interacting with other traffic participants safely and smoothly. A promising approach to handle such traffic interactions is equipping autonomous vehicles with interaction-aware controllers (IACs). These controllers predict how surrounding human drivers will respond to the autonomous vehicle’s actions, based on a driver model. However, the predictive validity of driver models used in IACs is rarely validated, which can limit the interactive capabilities of IACs outside the simple simulated environments in which they are demonstrated. In this article, we argue that besides evaluating the interactive capabilities of IACs, their underlying driver models should be validated on natural human driving behavior. We propose a workflow for this validation that includes scenario-based data extraction and a two-stage (tactical/operational) evaluation procedure based on human factors literature. We demonstrate this workflow in a case study on an inverse-reinforcement-learning-based driver model replicated from an existing IAC. This model only showed the correct tactical behavior in 40% of the predictions. The model’s operational behavior was inconsistent with observed human behavior. The case study illustrates that a principled evaluation workflow is useful and needed. We believe that our workflow will support the development of appropriate driver models for future automated vehicles.