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Diverse and realistic traffic scenarios are crucial for evaluating the AIsafety of autonomous driving systems in simulation. This work introduces adata-driven method called TrafficGen for traffic scenario generation. It learnsfrom the fragmented human driving data collected in the real world and then cangenerate realistic traffic scenarios. TrafficGen is an autoregressivegenerative model with an encoder-decoder architecture. In each autoregressiveiteration, it first encodes the current traffic context with the attentionmechanism and then decodes a vehicle's initial state followed by generating itslong trajectory. We evaluate the trained model in terms of vehicle placementand trajectories and show substantial improvements over baselines. TrafficGencan be also used to augment existing traffic scenarios, by adding new vehiclesand extending the fragmented trajectories. We further demonstrate thatimporting the generated scenarios into a simulator as interactive trainingenvironments improves the performance and the safety of driving policy learnedfrom reinforcement learning. More project resource is available athttps://metadriverse.github.io/trafficgen

TrafficGen: Learning to Generate Diverse and Realistic Traffic Scenarios