The Industrial Kinematic Trajectory Model (IKTM) is a new coordinate‑free autoregressive generator that models industrial vehicle motion using kinematic sequences of speed and heading change, eliminating the need for absolute spatial references. It employs a causal transformer with probabilistic mixture heads and a duration‑conditioning signal, extending a prior Markovian approach with deep sequence modeling. Trained on a single site, IKTM achieves near‑optimal turn‑rate distribution matching and low Jensen‑Shannon divergence across four sites, outperforming the earlier model by 5.3–6.0× in oracle‑length protocols while maintaining exact duration tracking in rollouts.
arXiv:2607. 20549v1 Announce Type: new Abstract: Trajectory datasets used in ADAS evaluation are heavily biased toward routine driving; genuine vehicle-to-vehicle conflict events are rare, and the rarer the event, the higher the cost when an ADAS system fails to handle it.
arXiv:2609.13941v1 Announce Type: new Abstract: We present BLInD (Blind Learned Intent Distribution), a compact network that maps recent vehicle-state history (e.g. speed, curvature, indicator, and v...
The paper introduces the Markov Chain Car‑Following (MC‑CF) model, an empirical probabilistic approach that represents car‑following as a Markov process and samples accelerations from empirical distributions within discretized state bins. Evaluation on the Waymo Open Motion Dataset shows that MC‑CF variants outperform physics‑based baselines and compete with modern data‑driven methods in both one‑step and open‑loop trajectory prediction. Zero‑shot transfer to the Naturalistic Phoenix dataset and microscopic ring‑road simulations demonstrate cross‑domain generalization and scalability, with the model reducing collisions and reproducing naturalistic shockwave propagation.
arXiv:2606. 31209v1 Announce Type: new Abstract: Interactive traffic simulation is a vital world model for autonomous driving.
The paper introduces Nomad, a transfer-and-ground framework for generating human mobility trajectories without target-city trajectory data. It learns relative transitions from source cities using POI attributes and then grounds these transitions onto a target city’s POI map via a behavior graph and exploration–return walk. Experiments across ten cities show Nomad improves trajectory fidelity and downstream utility by roughly 15% and 3% respectively over adaptation baselines.
The paper introduces Bridge Graphical Models (BGMs), a framework that decomposes continuous‑time generative models into independent design choices: endpoint coupling, bridge law, Markovian projection, and current‑preserving dynamics. It defines the Markovization gap as the time‑integrated conditional variance of bridge velocity given the Markov state, quantifying an irreducible loss before training. Experiments on synthetic, latent, and pixel‑space tasks (CIFAR‑10 and Fashion‑MNIST) show that a feature‑space proxy of this gap, estimated quickly before training, predicts downstream training loss and FID in the same direction as full training results.
arXiv:2606. 25122v1 Announce Type: cross Abstract: Autonomous aerial vehicles operating in shared airspace must predict the future positions of non-cooperative obstacles to plan evasive maneuvers before a collision becomes unavoidable.
arXiv:2605.12805v2 Announce Type: replace-cross Abstract: MeanFlow enables one-step generation in continuous spaces by learning an average velocity over a time interval rather than the instantaneous...
arXiv:2606. 05130v1 Announce Type: cross Abstract: Individual-level mobility prediction is central to urban simulation, transportation planning, and policy analysis.
Hi-FLoop introduces a hierarchical state‑feedback framework for multi‑agent traffic simulation that reconciles decision time scales over an 8‑second rollout. The model uses eight scene‑level Worlds to maintain joint hypotheses, with an 8‑second Goal, 2‑second Preview, and 1‑second Control hierarchy, and commits only executed prefixes every 0.5 seconds to preserve factual consistency. A joint preview interaction graph and a prefix‑frozen A‑to‑B cascade enable sparse interaction refinement and accurate state recovery, achieving an overall score of 0.689987 on the H‑D public‑validation split and strong oracle‑minADE performance. whyItMatters":"The paper presents a novel multi‑timescale approach that improves consistency and realism in long‑horizon traffic simulations, as evidenced by its competitive evaluation metrics."
arXiv:2606. 18367v1 Announce Type: new Abstract: Standard benchmarks evaluate time series foundation models (TSFMs) using aggregate metrics, but these can mask severe failures in critical operating regimes.
arXiv:2609.36845v1 Announce Type: cross Abstract: Uncrewed aerial vehicle base stations (UAV-BSs) are expected to cover traffic demand that shifts across space and time, yet most repositioning scheme...