  This paper presented DriveArena, the first high-fidelity closed-loop
simulation system designed for driving agents navigating in real scenarios.
DriveArena features a flexible, modular architecture, allowing for the seamless
interchange of its core components: Traffic Manager, a traffic simulator
capable of generating realistic traffic flow on any worldwide street map, and
World Dreamer, a high-fidelity conditional generative model with infinite
autoregression. This powerful synergy empowers any driving agent capable of
processing real-world images to navigate in DriveArena's simulated environment.
The agent perceives its surroundings through images generated by World Dreamer
and output trajectories. These trajectories are fed into Traffic Manager,
achieving realistic interactions with other vehicles and producing a new scene
layout. Finally, the latest scene layout is relayed back into World Dreamer,
perpetuating the simulation cycle. This iterative process fosters closed-loop
exploration within a highly realistic environment, providing a valuable
platform for developing and evaluating driving agents across diverse and
challenging scenarios. DriveArena signifies a substantial leap forward in
leveraging generative image data for the driving simulation platform, opening
insights for closed-loop autonomous driving. Code will be available soon on
GitHub: https://github.com/PJLab-ADG/DriveArena
