  Simulation is the key to scaling up validation and verification for robotic
systems such as autonomous vehicles. Despite advances in high-fidelity physics
and sensor simulation, a critical gap remains in simulating realistic behaviors
of road users. This is because, unlike simulating physics and graphics,
devising first principle models for human-like behaviors is generally
infeasible. In this work, we take a data-driven approach and propose a method
that can learn to generate traffic behaviors from real-world driving logs. The
method achieves high sample efficiency and behavior diversity by exploiting the
bi-level hierarchy of driving behaviors by decoupling the traffic simulation
problem into high-level intent inference and low-level driving behavior
imitation. The method also incorporates a planning module to obtain stable
long-horizon behaviors. We empirically validate our method, named Bi-level
Imitation for Traffic Simulation (BITS), with scenarios from two large-scale
driving datasets and show that BITS achieves balanced traffic simulation
performance in realism, diversity, and long-horizon stability. We also explore
ways to evaluate behavior realism and introduce a suite of evaluation metrics
for traffic simulation. Finally, as part of our core contributions, we develop
and open source a software tool that unifies data formats across different
driving datasets and converts scenes from existing datasets into interactive
simulation environments. For additional information and videos, see
https://sites.google.com/view/nvr-bits2022/home
