arXiv Computer Vision
Expand-and-Prune: Test-Time Candidate-Space Adaptation for End-to-End Autonomous Driving
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arXiv AI
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Guiding End-to-End Driving Models with Endpoint-Constrained Trajectory Optimization
The paper introduces Endpoint-Constrained Optimization (ECO), a lightweight postprocessing layer that corrects intermediate waypoints of end-to-end driving policies while preserving the predicted endpoint. ECO does not require maps, privileged simulator state, or additional training, and can be applied to a wide range of waypoint-emitting policies. Experiments on two closed-loop simulators show that ECO significantly improves closed-loop performance, achieving top results in the HUGSIM Closed-Loop Driving Challenge and boosting scene scores on AlpaSim.
arXiv AI
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Pivot-Centric Trajectory Prediction: Bridging Long Horizons via Dynamical Guidance
arXiv:2608. 03521v1 Announce Type: cross Abstract: Forecasting precise future motion of surrounding agents is essential for reliable autonomous vehicles.
arXiv Computer Vision
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AlignDrive: Aligned Lateral-Longitudinal Planning for End-to-End Autonomous Driving
arXiv:2601.01762v4 Announce Type: replace-cross Abstract: Practical autonomous driving requires models that generalize by reasoning through spatial-temporal possibilities to exclude unsafe outcomes....
arXiv Computer Vision
Sep 3
Designing Versatile Samples for Learned Trajectory Scoring
The paper proposes a new training dataset that generates more informative positive and negative samples for trajectory scoring in autonomous driving. By perturbing logged human trajectories laterally toward the drivable boundary and longitudinally toward a leading vehicle, the dataset provides richer supervision than the planner’s default proposal pool. Using a transformer-based scorer trained on this dataset, the authors achieve improved EPDMS scores on two frozen planners, DiffusionDrive and MeanFuser, when evaluated on the NAVSIM navtrain dataset.
arXiv Machine Learning
Oct 9
PlanWAM: Planning-Shaped Future Representations for End-to-End Autonomous Driving
PlanWAM introduces a Planning‑Shaped World Action Model that tailors future‑state representations to be most useful for trajectory planning in end‑to‑end autonomous driving. It compresses historical observations with a Temporal Register Pyramid, shapes a future latent representation using a privileged posterior branch guided by planning objectives, and distills this into a prior branch that predicts the representation from history alone. The resulting foresighted planning context yields state‑of‑the‑art performance on NAVSIM and HUGSIM benchmarks.
arXiv Computer Vision
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Do Better Visual Representations Always Lead to Better End-to-End Autonomous Driving?
arXiv:2610.09695v1 Announce Type: cross Abstract: Visual foundation models (VFMs) are increasingly integrated into end-to-end autonomous driving for their powerful representations, yet it remains unc...