Gap Approaching Intelligent Driver Model for Interactive Simulation of Merging Scenarios

As an important part of automated vehicle development and testing, simulation makes heavy use of driver models to reproduce the behavior of traffic participants. Due to their simplicity, most models fail to capture driver behavior in interactive situations like lane changes or merging, where drivers need to consider multiple vehicles simultaneously and smoothly approach gaps. We propose the Gap APproaching Intelligent Driver Model (GAP-IDM), an extension of IDM that takes an arbitrary number of target vehicles into account and produces realistic behavior for approaching traffic gaps, even when the ego vehicle has to overtake or fall behind target vehicles. To this end, we use a target distance rectification to produce smooth behaviors even for small or negative distances, and to enforce time or distance limits on the maneuver. We evaluate the proposed model in an optional and a necessary lane change scenario and demonstrate that it generates realistic driving behavior. Possible applications of our model include simulations of interactive scenarios, development of complex driver models with multiple target vehicles, or the use as a low-level policy in a high-level behavior planning module.