The paper introduces Anticipatory Generative Modeling, a new graph-constrained approach for generating road‑network trajectories that focuses on behavior modeling rather than simple location simulation. It presents Foresight, a probabilistic model that iteratively anticipates, selects, updates, and re‑anticipates future transitions to produce realistic routes that stay on the road network. Experiments show that Foresight outperforms existing state‑of‑the‑art generative methods.
arXiv:2610.18677v1 Announce Type: cross Abstract: Online decision-making faces a tension between efficient inference and long-horizon reasoning. Value-based methods rely on short-horizon bootstrappin...
arXiv:2606. 02287v1 Announce Type: cross Abstract: Urban trajectory generation is a fundamental task for transportation simulation, urban planning, and mobility analytics.
arXiv:2508. 17218v4 Announce Type: replace Abstract: Correlated link travel times create decision-relevant patterns in partial route histories.
arXiv:2609.07206v1 Announce Type: cross Abstract: Trajectory representation learning underpins a wide range of trajectory analytics tasks; however, most existing self-supervised approaches, whether d...
arXiv:2609.37098v1 Announce Type: cross Abstract: Vehicle-infrastructure cooperation can complement onboard sensing with broader and more informative observations of the traffic environment, providin...
arXiv:2608. 14570v1 Announce Type: new Abstract: Understanding human mobility is critical for a wide range of urban applications, including traffic management, epidemic control, and urban planning.
arXiv:2606. 11019v1 Announce Type: cross Abstract: Learning-based motion planners, despite recent progress, often suffer from temporal inconsistency.
The paper introduces Bayesian Flow Networks for Offline Trajectory Planning (BFN-RL), a generative modeling framework that unifies discrete and continuous trajectory synthesis for offline reinforcement learning. Unlike prior diffusion models that rely on Gaussian noise, BFN-RL iteratively updates distribution parameters, enabling a categorical planner to produce future state sequences and an inverse-dynamics model to translate these states into actions. Experiments demonstrate that BFN-RL effectively generates trajectories in both discrete planning and continuous control tasks, highlighting its versatility across data modalities.
GRID‑Planner introduces a graph‑structured relational reward shaping framework for diffusion‑based autonomous driving planning. By integrating graph‑aware importance weighting, a time‑indexed relational reward surrogate, and an advantage‑weighted diffusion objective with on‑policy self‑distillation, it forms a rollout‑return‑advantage‑update loop that eliminates explicit likelihoods. Experiments on nuPlan and interPlan demonstrate significant reductions in collision rate, lane deviation, and jerk, along with a 3.4% improvement in overall planning score.
Learning-based motion planners, despite recent progress, often suffer from temporal inconsistency. Small perturbations across frames can accumulate into unstable trajectories, degrading comfort and safety in closed-loop driving.
arXiv:2606. 14956v1 Announce Type: new Abstract: Autonomous driving systems rely on precise trajectory prediction to plan safe and efficient movement.
arXiv:2606. 06423v1 Announce Type: cross Abstract: Safety-critical traffic scenario generation is essential for evaluating autonomous driving systems under rare but high-risk interactions.