title: GeoDrive: 3D Geometry-Informed Driving World Model with Precise Action
    Control
summary: 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.
paper_authors._id: 68391dbb0653b6a3441a7f7e
paper_authors.name: Anthony Chen
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-30T06:53:38.940Z
paper_authors._id: 6311d9ee04f842f79916158c
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/PvOYgDEpe0x5ExLyj1BK-.png
paper_authors.fullname: chen
paper_authors.type: user
paper_authors.user: antonio-c
paper_authors._id: 68391dbb0653b6a3441a7f7f
paper_authors.name: Wenzhao Zheng
paper_authors._id: 68391dbb0653b6a3441a7f80
paper_authors.name: Yida Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-30T06:53:36.769Z
paper_authors._id: 647068944be5cf1f33491cb9
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/Aqudl2PTSKPAylBhlccWr.png
paper_authors.fullname: Yida Wang
paper_authors.type: user
paper_authors.user: wangyida
paper_authors._id: 68391dbb0653b6a3441a7f81
paper_authors.name: Xueyang Zhang
paper_authors._id: 68391dbb0653b6a3441a7f82
paper_authors.name: Kun Zhan
paper_authors._id: 68391dbb0653b6a3441a7f83
paper_authors.name: Peng Jia
paper_authors._id: 68391dbb0653b6a3441a7f84
paper_authors.name: Kurt Keutzer
paper_authors._id: 68391dbb0653b6a3441a7f85
paper_authors.name: Shanghang Zhang
paper_title: GeoDrive: 3D Geometry-Informed Driving World Model with Precise Action
    Control
paper_summary: 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.
paper_ai_summary: GeoDrive integrates robust 3D geometry into driving world models to improve spatial understanding and action controllability in autonomous navigation, enhancing safety and reliability.
paper_ai_keywords: world models dynamic environment simulation autonomous driving 3D geometric consistency occlusion handling 3D representation 2D rendering ego-car trajectory dynamic editing module action accuracy 3D spatial awareness scene modeling object editing object trajectory control