Adversarial Safety-Critical Scenario Generation using Naturalistic Human Driving Priors

Overview

The method combines calibrated driver models, adversarial imitation learning and reinforcement learning to generate challenging traffic interactions. NGSIM-based evaluations compare collision exposure and driving behavior to balance naturalism with adversarial criticality.