🤖 AI Summary
This work addresses the inefficiency in autonomous driving testing caused by cumbersome workflows and redundant code during complex scenario construction. To overcome these limitations, the authors propose Modular2Simple, a novel tool that introduces a modular-composition paradigm for scenario generation. By combining simple or modular OpenSCENARIO scenes, Modular2Simple enables flexible and efficient creation of diverse, complex test scenarios while strictly adhering to the OpenSCENARIO standard. The approach seamlessly integrates with mainstream simulation platforms such as CARLA, significantly enhancing scenario reusability and customizability while reducing development complexity. Experimental results demonstrate that, compared to conventional methods, the proposed solution substantially decreases both development time and labor costs, markedly improving the efficiency and diversity of test scenario construction.
📝 Abstract
The rapid advancement of autonomous driving systems (ADS) has introduced significant challenges, particularly in the creation of realistic and complex scenarios for testing and validation. This paper introduces Modular2Simple, a tool designed to address these challenges by simplifying and enhancing the process of creating complex ADS scenarios. Modular2Simple seamlessly integrates with the CARLA simulator and is applicable to any software that supports the OpenSCENARIO format. By leveraging existing simple scenarios in the OpenSCENARIO format, the tool enables developers to create easily customizable modular scenarios through the combination of multiple simple or modular scenarios, significantly simplifying the scenario creation process while maintaining flexibility in scenario design. This approach not only facilitates the development of complex scenarios, reducing both development time and effort, but also promotes scenario reuse and customization, which leads to a significant reduction in code complexity and enhanced efficiency in scenario design and testing compared to traditional scenario development methods.