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

• URL: http://arxiv.org/abs/2408.00415v1
• Date: Thu, 1 Aug 2024 09:32:01 GMT
• Title: DriveArena: A Closed-loop Generative Simulation Platform for Autonomous Driving
• Authors: 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,
• Abstract summary: DriveArena is a high-fidelity closed-loop simulation system designed for driving agents navigating in real scenarios. It features 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.
• Score: 30.024309081789053
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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

Related papers

• DriveDreamer: Towards Real-world-driven World Models for Autonomous Driving [76.24483706445298]
  We introduce DriveDreamer, a world model entirely derived from real-world driving scenarios. In the initial phase, DriveDreamer acquires a deep understanding of structured traffic constraints, while the subsequent stage equips it with the ability to anticipate future states. DriveDreamer enables the generation of realistic and reasonable driving policies, opening avenues for interaction and practical applications.
  arXiv  Detail & Related papers  (2023-09-18T13:58:42Z)
• Language Conditioned Traffic Generation [37.71751991840586]
  LCTGen is a large language model with a transformer-based decoder architecture that selects likely map locations from a dataset of maps. It produces an initial traffic distribution, as well as the dynamics of each vehicle. LCTGen outperforms prior work in both unconditional and conditional traffic scene generation in terms of realism and fidelity.
  arXiv  Detail & Related papers  (2023-07-16T05:10:32Z)
• TrafficBots: Towards World Models for Autonomous Driving Simulation and Motion Prediction [149.5716746789134]
  We show data-driven traffic simulation can be formulated as a world model. We present TrafficBots, a multi-agent policy built upon motion prediction and end-to-end driving. Experiments on the open motion dataset show TrafficBots can simulate realistic multi-agent behaviors.
  arXiv  Detail & Related papers  (2023-03-07T18:28:41Z)
• Sim-to-Real via Sim-to-Seg: End-to-end Off-road Autonomous Driving Without Real Data [56.49494318285391]
  We present Sim2Seg, a re-imagining of RCAN that crosses the visual reality gap for off-road autonomous driving. This is done by learning to translate randomized simulation images into simulated segmentation and depth maps. This allows us to train an end-to-end RL policy in simulation, and directly deploy in the real-world.
  arXiv  Detail & Related papers  (2022-10-25T17:50:36Z)
• DriveGAN: Towards a Controllable High-Quality Neural Simulation [147.6822288981004]
  We introduce a novel high-quality neural simulator referred to as DriveGAN. DriveGAN achieves controllability by disentangling different components without supervision. We train DriveGAN on multiple datasets, including 160 hours of real-world driving data.
  arXiv  Detail & Related papers  (2021-04-30T15:30:05Z)
• TrafficSim: Learning to Simulate Realistic Multi-Agent Behaviors [74.67698916175614]
  We propose TrafficSim, a multi-agent behavior model for realistic traffic simulation. In particular, we leverage an implicit latent variable model to parameterize a joint actor policy. We show TrafficSim generates significantly more realistic and diverse traffic scenarios as compared to a diverse set of baselines.
  arXiv  Detail & Related papers  (2021-01-17T00:29:30Z)
• LiDARsim: Realistic LiDAR Simulation by Leveraging the Real World [84.57894492587053]
  We develop a novel simulator that captures both the power of physics-based and learning-based simulation. We first utilize ray casting over the 3D scene and then use a deep neural network to produce deviations from the physics-based simulation. We showcase LiDARsim's usefulness for perception algorithms-testing on long-tail events and end-to-end closed-loop evaluation on safety-critical scenarios.
  arXiv  Detail & Related papers  (2020-06-16T17:44:35Z)
• SurfelGAN: Synthesizing Realistic Sensor Data for Autonomous Driving [27.948417322786575]
  We present a simple yet effective approach to generate realistic scenario sensor data. Our approach uses texture-mapped surfels to efficiently reconstruct the scene from an initial vehicle pass or set of passes. We then leverage a SurfelGAN network to reconstruct realistic camera images for novel positions and orientations of the self-driving vehicle.
  arXiv  Detail & Related papers  (2020-05-08T04:01:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.