Encounter Scenario Generation and Simulation Based on a Wasserstein Generative Adversarial Network with Gradient Penalty for Autonomous Ships

Virtual testing is one of the key methods for evaluating the collision avoidance capabilities of autonomous ships, and test scenarios provide the fundamental basis for its implementation. However, generating test scenarios that are both diverse and physically realistic remains a significant challenge in this field. This paper proposes a ship encounter scenario generation and dynamic simulation method for encounter situations based on a Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP). Specifically, the proposed framework consists of a Collision Risk Index-guided-WGAN-GP (CRI-WGAN-GP) for initial encounter scenario generation and a Physics-Informed Conditional WGAN-GP (PI-CWGAN-GP) for dynamic encounter scenario simulation. In the first stage, the CRI-WGAN-GP is developed to generate initial encounter scenarios with collision-risk characteristics. The Collision Risk Index (CRI) is directly embedded into the discriminator of the WGAN-GP. This mechanism forces the latent space to learn risk correlations, enabling effective exploration of high-risk boundaries and generating test cases with collision risks for the target ship. In the second stage, the PI-CWGAN-GP is designed to generate sequential dynamic simulation scenarios conditioned on the current encounter situation. By incorporating constraints related to ship motion performance, the PI-CWGAN-GP ensures that the generated ship behaviors are physically realistic. The proposed method is experimentally evaluated based on scenario diversity and physical realism, and the experimental results demonstrate that the proposed method can generate initial scenarios with collision risks and simulate ship behavior in encounter situations. Compared to existing random sampling and general generative adversarial network methods, the proposed approach shows significant advantages in generating more realistic and high-risk scenarios. In addition, a stress-test analysis is conducted based on a real encounter scenario, with the historical own-ship trajectory replayed as a reference response. Since no specific collision-avoidance algorithm is exposed to the generated scenarios in closed loop, the evaluation is limited to examining the encounter pressure imposed by the generated target-ship behaviors. The results show that the generated counterpart scenarios reduce the original spatial and temporal safety margins and create more demanding encounter conditions.