6 papers
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…
iFinder: Structured Zero-Shot Vision-Based LLM Grounding for Dash-Cam Video Reasoning
Manyi Yao, Bingbing Zhuang, Sparsh Garg +4
Grounding large language models (LLMs) in domain-specific tasks like post-hoc dash-cam driving video analysis is challenging due to their general-purpose training and lack of struc…
AutoScape: Geometry-Consistent Long-Horizon Scene Generation
Jiacheng Chen, Ziyu Jiang, Mingfu Liang +5
This paper proposes AutoScape, a long-horizon driving scene generation framework. At its core is a novel RGB-D diffusion model that iteratively generates sparse, geometrically cons…
Mapillary Vistas Validation for Fine-Grained Traffic Signs: A Benchmark Revealing Vision-Language Model Limitations
Sparsh Garg, Abhishek Aich
Obtaining high-quality fine-grained annotations for traffic signs is critical for accurate and safe decision-making in autonomous driving. Widely used datasets, such as Mapillary,…