GeoDrive: 3D Geometry-Informed Driving World Model with Precise Action
  Control
Authors: name: Anthony Chen, name: Wenzhao Zheng, name: Yida Wang, name: Xueyang Zhang, name: Kun Zhan, name: Peng Jia, name: Kurt Keutzer, name: Shanghang Zhang

Recent advancements in world models have revolutionized dynamic environment
simulation, allowing systems to foresee future states and assess potential
actions. In autonomous driving, these capabilities help vehicles anticipate the
behavior of other road users, perform risk-aware planning, accelerate training
in simulation, and adapt to novel scenarios, thereby enhancing safety and
reliability. Current approaches exhibit deficiencies in maintaining robust 3D
geometric consistency or accumulating artifacts during occlusion handling, both
critical for reliable safety assessment in autonomous navigation tasks. To
address this, we introduce GeoDrive, which explicitly integrates robust 3D
geometry conditions into driving world models to enhance spatial understanding
and action controllability. Specifically, we first extract a 3D representation
from the input frame and then obtain its 2D rendering based on the
user-specified ego-car trajectory. To enable dynamic modeling, we propose a
dynamic editing module during training to enhance the renderings by editing the
positions of the vehicles. Extensive experiments demonstrate that our method
significantly outperforms existing models in both action accuracy and 3D
spatial awareness, leading to more realistic, adaptable, and reliable scene
modeling for safer autonomous driving. Additionally, our model can generalize
to novel trajectories and offers interactive scene editing capabilities, such
as object editing and object trajectory control.