DriveArena: A Closed-loop Generative Simulation Platform for Autonomous Driving

Xuemeng Yang, Licheng Wen, Tiantian Wei, Yukai Ma, Jianbiao Mei, Xin Li, Wenjie Lei, Daocheng Fu, Pinlong Cai, Min Dou, Liang He, Yong Liu, Botian Shi, Yu Qiao; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2025, pp. 26933-26943

This paper introduces DriveArena, the first high-fidelity closed-loop simulation system designed for driving agents navigating real-world scenarios. DriveArena comprises two core components: Traffic Manager, a traffic simulator capable of generating realistic traffic flow on any global street map, and World Dreamer, a high-fidelity conditional generative model with infinite auto-regression. DriveArena supports closed-loop simulation using road networks from cities worldwide, enabling the generation of diverse traffic scenarios with varying styles. This powerful synergy empowers any driving agent capable of processing real-world images to navigate in DriveArena 's simulated environment. Furthermore, DriveArena features a flexible, modular architecture, allowing for multiple implementations of its core components and driving agents. Serving as a highly realistic arena for these players, our work provides a valuable platform for developing and evaluating driving agents across diverse and challenging scenarios. DriveArena takes a significant leap forward in leveraging generative models for driving simulation platforms, opening new avenues for closed-loop evaluation of autonomous driving systems.