  Simulation of the real-world traffic can be used to help validate the
transportation policies. A good simulator means the simulated traffic is
similar to real-world traffic, which often requires dense traffic trajectories
(i.e., with a high sampling rate) to cover dynamic situations in the real
world. However, in most cases, the real-world trajectories are sparse, which
makes simulation challenging. In this paper, we present a novel framework
ImInGAIL to address the problem of learning to simulate the driving behavior
from sparse real-world data. The proposed architecture incorporates data
interpolation with the behavior learning process of imitation learning. To the
best of our knowledge, we are the first to tackle the data sparsity issue for
behavior learning problems. We investigate our framework on both synthetic and
real-world trajectory datasets of driving vehicles, showing that our method
outperforms various baselines and state-of-the-art methods.
