Guided Latent Spaces for Controllable Multi-Scenario Generation in Autonomous Driving (Student Abstract)

Abstract

Scenario-based testing is an important approach for the development and validation of autonomous driving systems, as it enables evaluation across different driving situations. Safety-critical scenarios are especially relevant, but they occur rarely in real-world data, which creates the need for generation methods. In this paper, we present a scalable AI-based approach based on a variational autoencoder that unifies the generation of different types of critical scenarios while introducing controllability through a structured latent space. The integration of unified generation and latent space control advances AI-based scenario generation towards practical use, thereby supporting the requirements of industrial validation pipelines.