DriveArena: A Closed-loop Generative Simulation Platform for Autonomous Driving
Xuemeng Yang, Licheng Wen, Yukai Ma, Jianbiao Mei, Xin Li, Tiantian, Wei, Wenjie Lei, Daocheng Fu, Pinlong Cai, Min Dou, Botian Shi, Liang He,, Yong Liu, Yu Qiao

TL;DR
DriveArena is a high-fidelity, modular closed-loop simulation platform for autonomous driving that integrates generative models and traffic simulation to enable realistic environment interactions for driving agents.
Contribution
It introduces a novel, flexible simulation system combining generative image models with traffic simulation for autonomous driving research.
Findings
Realistic traffic interactions achieved in simulation
Generative models enable diverse scenario creation
Closed-loop environment supports autonomous driving development
Abstract
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…
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Taxonomy
TopicsSimulation Techniques and Applications · Autonomous Vehicle Technology and Safety · Human-Automation Interaction and Safety
