arXiv:2610.34689v1 Announce Type: new Abstract: Knowledge distillation can transfer large multimodal models to compact models for autonomous driving, but uniform token-level matching can dilute plann...
arXiv:2606. 14010v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have shown strong potential for end-to-end autonomous driving by jointly modeling visual perception, language reasoning, explainability and action prediction.
arXiv:2601. 21288v2 Announce Type: replace Abstract: Autonomous driving is an important and safety-critical task, and recent advances in LLMs/VLMs have opened new possibilities for reasoning and planning in this domain.
arXiv:2607. 04179v1 Announce Type: cross Abstract: End-to-end Vision-Language Models (VLMs) show immense potential in autonomous driving.
The paper studies cross‑stage context passing in Graph Visual Question Answering for autonomous driving, focusing on the Perception, Prediction, and Planning stages. Two complementary approaches are evaluated on DriveLM‑nuScenes: an explicit prompt‑based conditioning strategy using a domain‑adapted 4B VLM that reduces NLI contradiction by up to 42.6%, and an implicit gated context projector that, with only ~0.5% of parameters updated, cuts planning‑stage NLI contradiction by 34% and boosts cross‑stage entailment by 50%. The explicit method offers a strong zero‑training baseline for surface consistency, while the implicit method delivers significant semantic gains, highlighting the potential of domain adaptation for further improvement.
arXiv:2606. 17362v1 Announce Type: cross Abstract: Autonomous driving has shifted towards end-to-end policy learning, where reliable, interpretable policy evaluation is a fundamental challenge as driving quality is highly context-dependent.
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:2509.13769v2 Announce Type: replace Abstract: While reasoning technology like Chain of Thought (CoT) has been widely adopted in Vision Language Action (VLA) models, it demonstrates promising ca...
arXiv:2609. 28366v1 Announce Type: cross Abstract: Vision-language models (VLMs) offer a promising approach to long-tail autonomous driving, but existing driving datasets provide limited supervision for connecting decision-critical visual evidence with reasoning and planning.
DriveMind is a multimodal large language model (MLLM) framework for autonomous driving that maintains multiple hypotheses about a traffic scene and updates their weights as new frames arrive, inspired by particle filtering. By propagating each hypothesis through a vision-language model, scoring against current observations, and resampling to preserve diversity, DriveMind improves driving performance on the Bench2Drive benchmark, raising the Driving Score from 68.50 to 81.37 and the Success Rate from 41.36% to 54.09%. The approach yields the largest gains in ambiguous scenarios such as unsignalized intersections and oncoming-traffic interactions, and also enhances fine‑grained interactive driving tasks like merging, emergency braking, and give‑way.
arXiv:2606. 07000v1 Announce Type: new Abstract: Recent post-training methods, particularly Reinforcement Learning with Verifiable Rewards (RLVR), have significantly enhanced the reasoning ability of Large Vision-Language Models (LVLMs).
End-to-end models that map multimodal inputs directly to future trajectories/maneuvers have emerged as an increasingly prominent research paradigm in autonomous driving. This class of models includes both Vision-Language-Action models and trajectory-generative planners.