Looking Ahead and Back: Anticipatory Generative Modeling for Road-Network Trajectories

Key points

• Trajectory generation underpins urban applications such as traffic management and urban planning.1
• Existing generative methods such as diffusion models simulate location sequences and fail short of next transition selection, even producing trajectories that drift off the road network.1
• To fill this gap, we advocate a fundamental shift: moving beyond location simulation toward behavior modeling on the road network.1
• Concretely, we propose a novel generative model named Foresight that formulates a probabilistic model for an "anticipate--select--update--re-anticipate" loop that enables longer-term foresight while continuing sequential generation along the historical trajectory.1

Sources (1)

• [1]Looking Ahead and Back: Anticipatory Generative Modeling for Road-Network Trajectories
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 9, 07:23 AM

  In this paper, we revisit trajectory generation through the lens of generative route planning, and introduce a new graph-constrained generative paradigm, Anticipatory Generative Modeling, which resolves next transition selection by incorporating the Anticipation of how the future route may unfold toward the destination.

  Trajectory generation underpins urban applications such as traffic management and urban planning.

Extractive summary: sentences quoted from the sources.