  Diverse and realistic traffic scenarios are crucial for evaluating the AI
safety of autonomous driving systems in simulation. This work introduces a
data-driven method called TrafficGen for traffic scenario generation. It learns
from the fragmented human driving data collected in the real world and then can
generate realistic traffic scenarios. TrafficGen is an autoregressive
generative model with an encoder-decoder architecture. In each autoregressive
iteration, it first encodes the current traffic context with the attention
mechanism and then decodes a vehicle's initial state followed by generating its
long trajectory. We evaluate the trained model in terms of vehicle placement
and trajectories and show substantial improvements over baselines. TrafficGen
can be also used to augment existing traffic scenarios, by adding new vehicles
and extending the fragmented trajectories. We further demonstrate that
importing the generated scenarios into a simulator as interactive training
environments improves the performance and the safety of driving policy learned
from reinforcement learning. More project resource is available at
https://metadriverse.github.io/trafficgen
