arXiv:2607. 05705v1 Announce Type: cross Abstract: Multi-agent motion prediction is essential for automated vehicles to understand the intentions of surrounding vehicles.
arXiv:2608. 03521v1 Announce Type: cross Abstract: Forecasting precise future motion of surrounding agents is essential for reliable autonomous vehicles.
M3-Former is a multimodal transformer framework that uses large language models to encode vessel static attributes and navigational intent as semantic priors for long‑term trajectory prediction. It builds a unified multimodal representation space, aligns static semantic information with dynamic trajectory features via self‑attention, and employs a dual‑granularity Mixture‑of‑Experts architecture to capture both global route planning and fine‑grained maneuvering behaviors. A Steering‑Weighted Cross‑Entropy loss further improves accuracy on sparse turning events, and experiments on a Danish AIS dataset show consistent improvements over state‑of‑the‑art baselines, reducing ADE and FDE by up to 5.1% in 4‑hour predictions.
arXiv:2606. 26661v1 Announce Type: cross Abstract: Motion forecasting is essential for autonomous driving systems to enable safe decision-making and planning in complex driving scenarios.
Motion forecasting is essential for autonomous driving systems to enable safe decision-making and planning in complex driving scenarios. While existing predictors excel at minimizing standard displacement errors, they often overlook the adherence to lane topology of multimodal predictions, particularly for lower-probability modes.
arXiv:2509.13577v3 Announce Type: replace-cross Abstract: Trustworthy trajectory prediction grounds autonomous vehicle (AV) safety, yet deployed models inevitably face out-of-distribution (OOD) scene...
arXiv:2607. 29031v1 Announce Type: cross Abstract: Existing autonomous-driving world models typically perform dense prediction of future videos, occupancy states, BEV representations, or agent motion.
arXiv:2604. 07126v2 Announce Type: replace-cross Abstract: Predicting vehicle trajectories plays an important role in autonomous driving, transportation safety analysis, traffic operations, etc.
arXiv:2601. 18537v4 Announce Type: replace-cross Abstract: Accurate long-horizon vessel trajectory prediction remains challenging due to compounded uncertainty from complex navigation behaviors and environmental factors.
arXiv:2606. 00857v1 Announce Type: cross Abstract: Accurate and reliable vehicle trajectory prediction is essential for safe autonomous driving.
arXiv:2509. 21004v3 Announce Type: replace Abstract: Flight trajectory prediction for multiple aircraft is essential and provides critical insights into how aircraft navigate within current air traffic flows.
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:2607. 27418v1 Announce Type: new Abstract: Long-term ship trajectory prediction is a fundamental capability for maritime safety and autonomous navigation.