arXiv:2602. 21172v3 Announce Type: replace Abstract: Vision-Language-Action (VLA) models are advancing autonomous driving by replacing modular pipelines with unified end-to-end architectures.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.
The paper introduces Latent Action Driving Annotations (LADA), a three‑stage pipeline that converts large amounts of unlabelled observation‑trajectory data into a language‑conditioned driving model. First, a latent action model with a vector‑quantised bottleneck learns a compact codebook of vehicle intents. Then, a small set of language‑annotated examples trains a vision‑language translator to map observations and instructions into this codebook, and finally a VLA is trained on observation‑latent‑action pairs across the full corpus. Using less than 5% of language annotations, LADA attains a Driving Score of 87.98 and a Success Rate of 70.46% on Bench2Drive, matching or surpassing fully supervised baselines.
arXiv:2606.08684v2 Announce Type: replace Abstract: We present BLUE, a minimal method for better language use in vision-language-action (VLA) models for autonomous driving (AD). Through extensive ana...
arXiv:2607. 03182v1 Announce Type: cross Abstract: Autonomous driving planning requires translating navigation intent, traffic rules, dynamic interactions, and language instructions into executable continuous trajectories.
arXiv:2604. 04917v3 Announce Type: replace-cross Abstract: What does it take to build a visual reasoner that works across charts, science, spatial understanding, and open-ended tasks?
arXiv:2607. 04179v1 Announce Type: cross Abstract: End-to-end Vision-Language Models (VLMs) show immense potential in autonomous driving.
The paper investigates how Vision‑Language‑Action (VLA) models can generalise across different driving environments and camera setups. It introduces a multi‑dataset training strategy and an auxiliary objective called BEV‑Forcing, which injects bird‑eye‑view spatial information into the VLA backbone to improve both in‑distribution and out‑of‑distribution performance on a limited number of camera rigs. The authors observe that while BEV‑Forcing helps when training data is scarce, its advantage diminishes as the number of training embodiments grows, suggesting that scaling diversity may reduce the impact of such auxiliary tasks.