  Simulation has the potential to massively scale evaluation of self-driving
systems enabling rapid development as well as safe deployment. To close the gap
between simulation and the real world, we need to simulate realistic
multi-agent behaviors. Existing simulation environments rely on heuristic-based
models that directly encode traffic rules, which cannot capture irregular
maneuvers (e.g., nudging, U-turns) and complex interactions (e.g., yielding,
merging). In contrast, we leverage real-world data to learn directly from human
demonstration and thus capture a more diverse set of actor behaviors. To this
end, we propose TrafficSim, a multi-agent behavior model for realistic traffic
simulation. In particular, we leverage an implicit latent variable model to
parameterize a joint actor policy that generates socially-consistent plans for
all actors in the scene jointly. To learn a robust policy amenable for long
horizon simulation, we unroll the policy in training and optimize through the
fully differentiable simulation across time. Our learning objective
incorporates both human demonstrations as well as common sense. We show
TrafficSim generates significantly more realistic and diverse traffic scenarios
as compared to a diverse set of baselines. Notably, we can exploit trajectories
generated by TrafficSim as effective data augmentation for training better
motion planner.
