arXiv AI
Jun 2
From Cues to Horizons: Dynamic Risk Horizon Profiling for Trajectory Prediction
arXiv:2606. 00857v1 Announce Type: cross Abstract: Accurate and reliable vehicle trajectory prediction is essential for safe autonomous driving.
Uncertainty-Aware Optimization for Physics-Aware Highway Trajectory Prediction
The paper introduces uncertainty-aware extensions of the X-TRACK physics‑aware trajectory prediction framework, namely X-TRACK-DE and X-TRACK-MCD. These extensions predict future vehicle motion variables while explicitly modeling and propagating both aleatoric and epistemic uncertainties through vehicle dynamics, rather than only in trajectory space. Conformal prediction is then applied to the trajectory‑space covariance to create uncertainty regions that achieve a desired marginal coverage level, and experiments on the highD dataset demonstrate improved prediction accuracy and calibrated uncertainty estimates.
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arXiv Machine Learning
Aug 11
CoCoNav: Conformal Control for Safe Robot Navigation in Crowds
arXiv:2608. 07751v1 Announce Type: cross Abstract: Safe and efficient robot navigation in crowds requires anticipating pedestrian motion despite uncertain and potentially shifting prediction errors.
arXiv Machine Learning
Oct 5
Localized Conformal Safety Monitoring with Vision-Language Models for Autonomous Driving
arXiv:2610.02765v1 Announce Type: cross Abstract: Monitoring planned driving trajectories requires accurately estimating the collision likelihood with actors whose motion is itself impacted by the eg...
arXiv Computer Vision
Sep 1
Uncertainty-Aware Trajectory Forecasting from Imperfect Tracking
arXiv:2608.30899v1 Announce Type: new Abstract: Most trajectory forecasting models are trained on clean annotated histories, and are often evaluated under the same idealized assumption, although prac...
arXiv AI
Jul 22
SKETCH: Semantic Key-Point Conditioning for Long-Horizon Vessel Trajectory Prediction
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.
Hugging Face Trending Papers
Jun 9
Diffusion Forcing Planner: History-Annealed Planning with Time-Dependent Guidance for Autonomous Driving
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 AI
Aug 20
DA-WAM: Decision-Aligned Future Latents for Driving World Models
DA‑WAM is a framework that integrates predictive representation learning, action‑conditioned future modeling, and trajectory scoring into a single decision‑making objective for autonomous driving. It uses an online encoder with a stable momentum target to keep future representations aligned with the driving task, generating a distinct future latent for each trajectory candidate. A future‑latent‑conditioned scorer evaluates these latents, with expert‑matched trajectories supervised by observed futures and safety‑critical hard negatives providing additional guidance, achieving state‑of‑the‑art results on NAVSIM‑v1 and NAVSIM‑v2.
arXiv AI
Jun 10
Diffusion Forcing Planner: History-Annealed Planning with Time-Dependent Guidance for Autonomous Driving
arXiv:2606. 11019v1 Announce Type: cross Abstract: Learning-based motion planners, despite recent progress, often suffer from temporal inconsistency.
arXiv Machine Learning
Aug 11
Particle-Based Conformal Prediction for Contact-Aware Uncertainty Calibration in Stratified Configuration Spaces
arXiv:2608. 09166v1 Announce Type: cross Abstract: Reliable uncertainty representation is essential for deploying autonomous systems that interact with their environment, as robots must reason about how uncertainty arising from both stochasticity and model mismatch is impacted by contacts with obstacles (e.
arXiv Machine Learning
Jun 10
Optimization-based Online Conformal Prediction for Multi-step Forecasting
arXiv:2508. 13362v2 Announce Type: replace Abstract: Conformal prediction (CP) is well-suited for uncertainty quantification in time series forecasting due to its distribution-free coverage guarantees.
arXiv Machine Learning
Sep 25
Active Client Selection in Federated Trajectory Prediction with Uncertainty-Awareness and Heterogeneous Complexity
The paper introduces active client selection strategies for federated learning in autonomous vehicle trajectory prediction, addressing challenges of high scene uncertainty and heterogeneous complexity across different driving environments. It proposes uncertainty-aware selectors that use per-client negative log-likelihood and aleatoric uncertainty, as well as a joint selector that balances scene complexity and uncertainty to prioritize informative clients. Experiments on the Argoverse dataset show that federated models outperform local training, with uncertainty-aware selection speeding convergence and improving key metrics, while the joint selector yields the best generalization under strong heterogeneity.
arXiv Machine Learning
Sep 22
Marginal Calibration Does Not Compose: Hidden Dependence in Modular Robot Navigation
arXiv:2609.23731v1 Announce Type: cross Abstract: Robotic systems are typically composed of multiple independently developed modules that work together to perceive, predict, and act in the environmen...