Rank2Tell: A Multimodal Driving Dataset for Joint Importance Ranking and Reasoning
Open accessAbstract
The widespread adoption of commercial autonomous vehicles (AVs) and advanced driver assistance systems (ADAS) may largely depend on their acceptance by society, for which their perceived trustworthiness and interpretability to riders are crucial. In general, this task is challenging because modern autonomous systems software relies heavily on black-box artificial intelligence models. Towards this goal, this paper introduces a novel dataset, Rank2Tell1, a multi-modal ego-centric dataset for Ranking the importance level and Telling the reason for the importance. Using various close and open-ended visual question answering, the dataset provides dense annotations of various semantic, spatial, temporal, and relational attributes of various important objects in complex traffic scenarios. The dense annotations and unique attributes of the dataset make it a valuable resource for researchers working on visual scene understanding and related fields. Furthermore, we introduce a joint model for joint importance level ranking and natural language captions generation to benchmark our dataset and demonstrate performance with quantitative evaluations.
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Transformer models have made notable strides in computer vision, as evidenced by vision transformers like EfficientViT and YOLOv8.
“These models have also made notable strides in computer vision, as evidenced by the development of vision transformers like EfficientViT [145], [146] and YOLOv8 [147], [148], [149].”
From From Google Gemini to OpenAI Q* (Q-Star): A Survey on Reshaping the Generative Artificial Intelligence (AI) Research Landscape (2025) · cites this paper as [146]
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References 45 · Cited by 63
- Authors
- Sachdeva, E., Agarwal, N., Chundi, S., Roelofs, S., Li, J., Kochenderfer, M., Choi, C., & Dariush, B.
- Affiliations
- Honda (United States) (US)
- Stanford University (US)
- Venue
- 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
- Publisher
- IEEE
- Published
- 2024-01-03
- Pages
- 7498-7507
- ISSN
- 2472-6737 (ISSN-L)
- OpenAlex
- W4394596424
- Cited by
- 63
- References
- 45
- Citations by year
- 2026: 18 · 2025: 25 · 2024: 19 · 2023: 1
- FWCI
- 12.80
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Winter Conference on Applications of Computer Vision (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by-nc-sa)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- driving data, importance ranking, visual question answering, autonomous vehicles, driver assistance systems, interpretability, black-box AI, traffic scene understanding, scene understanding
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