DTTF-Sim: A Digital Twin-Based Simulation System for Continuous Autonomous Driving Testing

As autonomous driving technology matures, the focus shifts to enhancing the safety and reliability of these systems. Simulation testing is a critical method for efficiently and rapidly validating the performance of autonomous vehicles (AVs). A robust AV system requires extensive testing across a wide range of scenarios and iterative improvements. However, current simulation systems have limitations in supporting diverse scenarios, often relying on expert-designed situations. To address these challenges, we introduce DTTF-Sim, a novel simulation system based on Digital Twin technology for traffic flow. DTTF-Sim aims to accurately replicate real-world traffic flow conditions, offering continuous long-term simulation capabilities for AV testing. The system can simulate detailed dynamic traffic scenarios with a focus on interactions between multiple vehicles and between AVs and background traffic vehicles, modeling the strategic decision-making processes that occur in these encounters. This paper outlines the architecture and functionalities of DTTF-Sim, highlighting its ability to overcome the shortcomings of existing simulation platforms. We demonstrate the effectiveness of DTTF-Sim through case studies and experimental results, showing its potential to significantly advance the development and testing of autonomous driving technologies.