Adversarial jaywalker modeling for simulation-based testing of Autonomous Vehicle Systems

We present an approach for creating adversarial jaywalkers, autonomous pedestrian models which intentionally act to create unsafe situations involving other vehicles. An adversarial jaywalker employs a hybrid state-model with social forces and state transition rules. The parameters (for social forces and state transitions) of this model are tuned via reinforcement learning to create risky situations faster with synthetic yet plausible behavior. The resulting jaywalkers are capable of realistic behavior while still engaging in sufficiently risky actions to be useful for testing. These adversarial pedestrian models are useful in a wide range of scenario-based tests for autonomous vehicles.