DriveArena: A Closed-loop Generative Simulation Platform for Autonomous Driving
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arXiv
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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866914895816032256 |
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| author | Yang, Xuemeng Wen, Licheng Ma, Yukai Mei, Jianbiao Li, Xin Wei, Tiantian Lei, Wenjie Fu, Daocheng Cai, Pinlong Dou, Min Shi, Botian He, Liang Liu, Yong Qiao, Yu |
| author_facet | Yang, Xuemeng Wen, Licheng Ma, Yukai Mei, Jianbiao Li, Xin Wei, Tiantian Lei, Wenjie Fu, Daocheng Cai, Pinlong Dou, Min Shi, Botian He, Liang Liu, Yong Qiao, Yu |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_00415 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DriveArena: A Closed-loop Generative Simulation Platform for Autonomous Driving Yang, Xuemeng Wen, Licheng Ma, Yukai Mei, Jianbiao Li, Xin Wei, Tiantian Lei, Wenjie Fu, Daocheng Cai, Pinlong Dou, Min Shi, Botian He, Liang Liu, Yong Qiao, Yu Robotics Artificial Intelligence Computer Vision and Pattern Recognition 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 |
| title | DriveArena: A Closed-loop Generative Simulation Platform for Autonomous Driving |
| topic | Robotics Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2408.00415 |