Intersection Complexity and Its Influence on Human Drivers

Abstract

As mixed traffic between automated vehicles and human drivers in inner city becomes more prevalent in the near future understanding and predicting drivers' behavior is important. Additionally, there is a wide variety of inner city intersections. They can differ greatly in traffic density, visibility, number of objects and many more aspects. This difference in complexity has an influence on the behavior of human drivers at intersections. To further understand the effect of complexity we conducted a naturalistic driving field study in inner city traffic with 34 participants. We focused on unsignalized intersections because there is a greater range of possibly ambiguous situations at such intersections than compared to e.g. an intersection regulated by traffic lights. Features describing the behavior (commit distance, drop in velocity and the minimal velocity) are extracted from the driven trajectories. Additionally, we define intersection complexity by several features describing an intersection. These features include both the static (street, visible and driveable width, the visibility of the other streets and the number of trees) and the dynamic environment (entry location and turning direction, numbers of vehicles, vehicles with interaction, vehicles with priority, vehicles having to yield and pedestrians). Based on those we show that the entry location and the turning direction have a significant effect on the behavior features. Additionally, we show that the typical behavior of human drivers can be predicted by the features describing an intersection's complexity. Finally, the feature set is reduced in dimensionality for a more condensed intersection description. For that we test reduced feature sets as well as feature sets from an autoencoder and show that prediction is feasible with them as well.