Hugging Face Trending Papers
TerraTransfer++: Demonstration-Free End-to-End Planner
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arXiv Machine Learning
Oct 23
TerraTransfer++: Demonstration-Free End-to-End Planner
TerraTransfer++ demonstrates a demonstration‑free, end‑to‑end autonomous driving framework that leverages self‑play in vectorized simulators to pretrain a policy. The method distills self‑play rollouts into a flow‑matching trajectory teacher and trains lightweight adapters on a frozen vision backbone, using action KL divergence, a batch‑relational low‑rank loss, and trajectory‑matching loss. The resulting closed‑loop policy surpasses all published imitation‑based methods on the HUGSim benchmark, while the planner achieves state‑of‑the‑art performance on NAVSIM navhard and navtest without any human demonstrations.
arXiv AI
Jun 17
TerraTransfer: Learning End-to-End Driving Policies Without Expert Demonstrations
arXiv:2606. 17386v1 Announce Type: cross Abstract: End-to-end autonomous driving has achieved state-of-the-art performance on benchmarks and real-world deployments.
arXiv Computer Vision
Sep 30
RoXDrive: Closed-Loop Reinforcement Learning for End-to-End Autonomous Driving via Action-Faithful Rollouts
arXiv:2609.36851v1 Announce Type: new Abstract: End-to-end autonomous driving policies are commonly trained via imitation learning on logged demonstrations without observing the consequences of their...
arXiv AI
Jul 15
TerraZero: Procedural Driving Simulation for Zero-Demonstration Self-Play at Scale
arXiv:2607. 13028v1 Announce Type: cross Abstract: Training robust autonomous driving agents requires a simulator that is fast enough for reinforcement learning at scale, realistic enough to ground behavior in real-world map structure, and diverse enough to cover the safety-critical long tail that logged data rarely contains.
arXiv Computer Vision
Oct 12
SimVTA: Simulation-Grounded Video-Trajectory Alignment for Driving World Action Models
SimVTA is a post‑training framework that enhances driving world action models by aligning simulated future videos with candidate trajectories. It samples trajectories, renders high‑reward futures in a real‑world simulator using 3D Gaussian Splatting, and prioritizes video supervision for high‑error futures. The approach improves planning performance, raising PDMS from 86.2 to 91.3 on NAVSIM v1 and achieving strong results on NAVSIM v2 and HUGSIM.
arXiv AI
Aug 3
RAPiD: Reward-Guided Consistency Distillation of Diffusion Planners for Real-Time Autonomous Driving
arXiv:2602. 07339v2 Announce Type: replace Abstract: Diffusion-based trajectory planners can model multi-modal driving behavior, but their iterative denoising process introduces a latency bottleneck for real-time closed-loop deployment.