20 citations · 56 across the 19 of their papers we have counts for
5 papers · 1 filter
HorizonComposer: Spatiotemporally Consistent Driving Video Editing with Enriched Traffic Semantics
Mauricio Soroco, Yuqiu Liu, Zaid Tasneem +5
Instruction-guided video generative models offer a scalable solution for stress-testing autonomous driving systems by simulating diverse environmental conditions. However, driving…
CAViAR: A Causal Video Dataset for Fine-Grained Accident Reasoning in Real-World Scenarios
Sparsh Garg, Yi-Wen Chen, Vijay Kumar B G +1
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 interpre…
Driving Video Retrieval for Complex Queries with Structured Grounding
Manyi Yao, Sparsh Garg, Christian Shelton +2
Video retrieval at scale is central to data curation and safety validation in autonomous driving, where users want to find not only scenes but also dynamic events such as cut-ins a…
What to Test Next: Interpretable Coverage Gap Discovery in Driving VLMs
Abhishek Aich, Sparsh Garg, Vijay Kumar BG +2
Driving vision-language models (VLMs) must accurately understand scenes across diverse conditions defined by Operational Design Domains (ODDs), yet verification remains sparse: man…
HorizonWeaver: Generalizable Multi-Level Semantic Editing for Driving Scenes
Mauricio Soroco, Francesco Pittaluga, Zaid Tasneem +5
Ensuring safety in autonomous driving requires scalable generation of realistic, controllable driving scenes beyond what real-world testing provides. Yet existing instruction guide…