Data‐driven human‐like cut‐in driving model using generative adversarial network

In this Letter, the authors developed a data‐driven human‐like cut‐in driving model using a generative adversarial network (GAN). When a vehicle cuts into the lane of another vehicle, complex interactions occur among the various vehicles. The purpose of the GAN driver model is to learn the human‐driving abilities in complex and diverse situations. Using the Kullback–Leibler divergence evaluation method, the authors confirmed that the GAN driver model shows more human‐like driving than the rule‐based driver model.