Hugging Face Trending Papers
Jun 23

UniDrive: A Unified Vision-Language and Grounding Framework for Interpretable Risk Understanding in Autonomous Driving

Recent multimodal large language models (MLLMs) have shown strong potential for autonomous driving scene understanding, yet existing methods still face a fundamental trade-off between temporal reasoning and spatial precision. Models that rely on single-frame or low-resolution inputs often miss small, distant, or partially occluded hazards, while language-centric driving models frequently provide limited grounded evidence for their explanations.

arXiv AI
Jun 24

UniDrive: A Unified Vision-Language and Grounding Framework for Interpretable Risk Understanding in Autonomous Driving

arXiv:2606. 24759v1 Announce Type: cross Abstract: Recent multimodal large language models (MLLMs) have shown strong potential for autonomous driving scene understanding, yet existing methods still face a fundamental trade-off between temporal reasoning and spatial precision.

By Xiaowei Gao, Pengxiang Li, Yitai Cheng, Ruihan Xu, James Haworth, Stephen Law, Yun Ye
Hugging Face Trending Papers
Jul 8

A knowledge-augmented dataset of high-risk driving scenarios with LLM annotations for autonomous driving

Safe autonomous driving requires both rapid responses to common high-risk events and deeper reasoning over rare, extreme long-tail scenarios in traffic safety. These scenarios are severely under-represented in naturalistic driving data, and existing trajectory and language-augmented datasets seldom provide high-risk event labels, semantic annotations, and verifiable safety signals.

arXiv Computation and Language
Oct 23

RiskCueBench: Benchmarking Anticipatory Reasoning from Early Risk Cues in Video-Language Models

RiskCueBench is a new video‑understanding benchmark that focuses on early risk cues in video‑language models. The dataset annotates the earliest moment in a video that signals a potential safety concern, rather than providing the full sequence that includes the accident. Experiments show a notable gap in current systems’ ability to interpret evolving situations and anticipate future risky events from these early visual signals.

By Sha Luo, Yogesh Prabhu, Timothy Ossowski, Kaiping Chen, Junjie Hu