DriveDreamer: Towards Real-world-driven World Models for Autonomous Driving

Kavli Affiliate: Zheng Zhu

| First 5 Authors: Xiaofeng Wang, Zheng Zhu, Guan Huang, Xinze Chen, Jiwen Lu

| Summary:

World models, especially in autonomous driving, are trending and drawing
extensive attention due to their capacity for comprehending driving
environments. The established world model holds immense potential for the
generation of high-quality driving videos, and driving policies for safe
maneuvering. However, a critical limitation in relevant research lies in its
predominant focus on gaming environments or simulated settings, thereby lacking
the representation of real-world driving scenarios. Therefore, we introduce
DriveDreamer, a pioneering world model entirely derived from real-world driving
scenarios. Regarding that modeling the world in intricate driving scenes
entails an overwhelming search space, we propose harnessing the powerful
diffusion model to construct a comprehensive representation of the complex
environment. Furthermore, we introduce a two-stage training pipeline. 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. The proposed DriveDreamer is the first world model
established from real-world driving scenarios. We instantiate DriveDreamer on
the challenging nuScenes benchmark, and extensive experiments verify that
DriveDreamer empowers precise, controllable video generation that faithfully
captures the structural constraints of real-world traffic scenarios.
Additionally, DriveDreamer enables the generation of realistic and reasonable
driving policies, opening avenues for interaction and practical applications.

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