arXiv AI
VLALight: A Vision-Language-Action Model for Traffic Signal Control
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arXiv AI
6d ago
VLALight: Lightweight Vision-Language-Action Models for Emergency-Aware Traffic Signal Control
VLALight is a lightweight end‑to‑end vision‑language‑action framework designed for traffic signal control. It fuses multiple camera views and textual instructions to directly predict signal actions, avoiding intermediate image‑to‑text conversions. The model, with only 0.5 B parameters, achieves superior emergency vehicle service, cutting pooled waiting time by 21.1% compared to cascaded methods while running in real time on local hardware.
arXiv AI
Jun 16
OmniTraffic: A Controllable Generation Pipeline and Benchmark for Spatio-Temporal Traffic Reasoning
arXiv:2606. 15749v1 Announce Type: cross Abstract: Traffic scene understanding requires models to reason beyond object recognition, including lane topology, multi-view geometry, temporal evolution, and signal-phase semantics.
arXiv AI
Jun 2
TrafficClaw: A Generalizable LLM Agent in the Unified Physical Environment for Urban Traffic Control
arXiv:2604. 17456v2 Announce Type: replace Abstract: Large language model (LLM) agents have shown strong capabilities in long-horizon reasoning, tool use, and decision-making in digital environments, yet extending them to physically grounded systems remains challenging.
arXiv AI
Aug 11
CMU-Drive and V2V-VLA: Cooperative Multi-agent Unified Driving with Reasoning Benchmark and Vehicle-to-Vehicle Vision-Language-Action Models
arXiv:2608. 07621v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have recently achieved impressive performance for end-to-end autonomous driving, yet existing approaches are primarily designed for an individual single autonomous driving agent with limited support for cooperative perception, reasoning, and planning.
arXiv Computer Vision
4d ago
V2X-WAM: A Cooperative World Action Model for End-to-End Autonomous Driving
arXiv:2609.37098v1 Announce Type: cross Abstract: Vehicle-infrastructure cooperation can complement onboard sensing with broader and more informative observations of the traffic environment, providin...
Hugging Face Trending Papers
Aug 18
Plug-and-Play Traffic Element Awareness for End-to-End Autonomous Driving
The paper introduces a plug‑and‑play method that injects traffic‑element signals—such as traffic lights and road signs—into end‑to‑end autonomous driving models with minimal architectural changes. By augmenting several public datasets with comprehensive traffic‑element annotations, the authors evaluate this integration across diverse driving paradigms, consistently improving performance on nuScenes, NAVSIM‑v1, NAVSIM‑v2, and Bench2Drive. The approach achieves a new state‑of‑the‑art result on the challenging NAVSIM‑v2 benchmark, demonstrating the broad utility of traffic‑element awareness.