Generating Critical Driving Scenarios through Generative Adversarial Networks: A Review

This review paper explores the applications of Generative Adversarial Networks (GANs) in generating critical driving scenarios for testing and validation of Advanced Driver Assistance Systems (ADAS) and Autonomous Vehicles (AVs). With road safety as a global concern and ADAS advancing rapidly, the need for comprehensive testing in diverse, high-risk scenarios is paramount. However, real-world in-depth accident data is often scarce, lacks diversity, and may not cover a broad range of safety-critical scenarios. This review provides a detailed analysis of GAN architectures – such as DCGAN, Cycle GAN, and TimeGAN – that are suitable for generating realistic synthetic driving scenarios. By leveraging these GAN models, synthetic scenarios mimicking real-world conditions can be produced, capturing both temporal and spatial features of pre-crash data. The paper also outlines how these synthetic scenarios can enhance the robustness of ADAS by enabling validation under rare and complex conditions, which are difficult to obtain from field data alone. Furthermore, the review organizes the literature across use cases like simulation-based testing, dataset augmentation, adversarial testing, and pedestrian interaction modeling, offering a structured understanding of research progress in this domain. It also emphasizes the value of synthetic data in advancing scenario diversity and driving model generalization. Additionally, this paper identifies challenges with current G AN applications, including issues related to training instability, scenario diversity, realism, and scalability. To address these limitations, the review suggests future research directions, including hybrid models, integration of multi-modal data, enhanced scenario validation frameworks, and ethical considerations for safe deployment. This review aims to broaden the understanding of GAN-based safety-critical driving scenario generation for robust ADAS and AD systems, emphasizing the importance of synthetic data in the evolving field of autonomous d riving technology and supporting both academic exploration and industrial implementation.