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:2608.28762v1 Announce Type: new Abstract: Recent advances in visual question answering (VQA) and multimodal large language models (MLLMs) have enabled natural-language reasoning over traffic sc...
arXiv:2609.36934v1 Announce Type: new Abstract: Traffic signal control (TSC) is essential for improving urban mobility and reducing congestion. Although roadside cameras are widely deployed at signal...
arXiv:2608. 16480v1 Announce Type: cross Abstract: We present RISE (Roadside Infrastructure Sequence Understanding and Evaluation), a framework spanning metric 3D tracking and structured vision-language reasoning in roadside sequences.
arXiv:2512. 05277v3 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) are increasingly deployed as the perception and reasoning backbone of autonomous agents acting in the wild, with autonomous driving (AD) being one of the most safety-critical instances.
arXiv:2503.13938v3 Announce Type: replace-cross Abstract: Comprehensive traffic scene understanding is a foundational capability for Intelligent Transportation Systems (ITS) underpinning applications...
arXiv:2512. 05277v4 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) are increasingly deployed as the perception and reasoning backbone of autonomous agents acting in the wild, with autonomous driving (AD) being one of the most safety-critical instances.
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:2511.20022v3 Announce Type: replace-cross Abstract: Recent advancements in multimodal large language models (MLLMs) have shown strong understanding of driving scenes, drawing interest in their...
TrafficImag is the first benchmark designed to evaluate counterfactual roadside traffic video generation, combining a large roadside dataset with an executable protocol that supports behavior reasoning, intervention-aware image editing, and conditional video generation. Each intervention is encoded as an actor-level program specifying target actor, intended behavior, legal route, interaction order, and temporal constraints, allowing a unified evaluation across diverse foundation models. The benchmark assesses four validity dimensions—initial-state correctness, route and behavior validity, interaction consistency, and non-target preservation—and reports that the best models achieve 80.4% macro F1 for reasoning and 55.0% end-to-end success when using a complete condition interface.
arXiv:2607. 08745v1 Announce Type: new Abstract: Recent advances in Vision-Language Models, Large Language Models, and Multimodal Large Language Models have improved autonomous driving tasks such as scene understanding, decision making, trajectory prediction, and visual question answering.
arXiv:2608. 19380v1 Announce Type: new Abstract: While modern autonomous driving systems excel at perception tasks such as object detection and trajectory prediction, they lack the high-level causal reasoning required to interpret traffic accidents.
arXiv:2606. 07708v1 Announce Type: cross Abstract: We introduce a dataset and benchmark for cross-view urban traffic perception built from synchronized ego-centric bicycle videos and aerial drone videos recorded at real urban intersections.