Real-World Data Inspired Interactive Connected Traffic Scenario Generation

Simulation is a crucial step in ensuring accurate, efficient, and realistic Connected and Autonomous Vehicles (CAVs) testing and validation. As the adoption of CAV accelerates, the integration of real-world data into simulation environments becomes increasingly critical. Among various technologies utilized by CAVs, Vehicle-to-Everything (V2X) communication plays a crucial role in ensuring a seamless transmission of information between CAVs, infrastructure, and other road users. However, most existing studies have focused on developing and testing communication protocols, resource allocation strategies, and data dissemination techniques in V2X. There is a gap where real-world V2X data is integrated into simulations to generate diverse and high-fidelity traffic scenarios. To fulfill this research gap, we leverage real-world Signal Phase and Timing (SPaT) data from Roadside Units (RSUs) to enhance the fidelity of CAV simulations. Moreover, we developed an algorithm that enables Autonomous Vehicles (AVs) to respond dynamically to real-time traffic signal data, simulating realistic V2X communication scenarios. Such high-fidelity simulation environments can generate multimodal data, including trajectory, semantic camera, depth camera, and bird's eye view data for various traffic scenarios. The generated scenarios and data provide invaluable insights into AVs' interactions with traffic infrastructure and other road users. This work aims to bridge the gap between theoretical research and practical deployment of CAVs, facilitating the development of smarter and safer transportation systems.