A Knowledge-Driven Method for Autonomous Driving Simulation Scene Generation

As the reliance on virtual simulation scenarios for autonomous driving testing and validation increases, traditional expert-driven scene enumeration generation methods can no longer meet the testing requirements. Digital virtual simulation scene generation methods offer significant technological advantages in terms of scene diversity, risk assessment, interpretability, and generation efficiency. They have become a key factor in enhancing the safety and reliability of testing and validation for autonomous driving technology. Consequently, they have become a prominent research focus in the field of automotive intelligence. This article delves into the crucial issue of scene generation in autonomous driving simulation. With the continuous advancement of autonomous driving technology, simulation has become a vital tool for testing, validating, and optimizing autonomous driving systems. The article notably introduces a knowledge-driven approach to personalized scene generation. It uses a hierarchical model to describe the relationships between different levels of scenes and analyzes the structure of OpenSCENARIO format files along with parameterization methods. This results in an automated script capable of generating specific scene files in bulk, tailored to testing requirements, thereby significantly enhancing the safety and efficiency of autonomous driving system development. However, the article also acknowledges that current research is in its early stages and can only generate simple scenes, potentially falling short of meeting the testing requirements for higher-level autonomous driving safety assessments. Future work will concentrate on developing knowledge-driven methods for generating critical scenes, aiming to improve the equivalence and rationality of specific scenes to address more advanced testing needs. This ongoing development holds significant promise for enhancing the safety and reliability of autonomous driving technology.