RoadscapesQA: A Multitask, Multimodal Dataset for Visual Question Answering on Indian Roads
This dataset addresses the need for research on visual question answering in unstructured driving environments, specifically for Indian roads, but it is incremental as it builds on existing multimodal dataset efforts.
The authors tackled the problem of visual scene understanding for autonomous driving by introducing Roadscapes, a dataset of up to 9,000 images from Indian roads with manually verified bounding boxes and generated question-answer pairs for tasks like object grounding and reasoning.
Understanding road scenes is essential for autonomous driving, as it enables systems to interpret visual surroundings to aid in effective decision-making. We present Roadscapes, a multitask multimodal dataset consisting of upto 9,000 images captured in diverse Indian driving environments, accompanied by manually verified bounding boxes. To facilitate scalable scene understanding, we employ rule-based heuristics to infer various scene attributes, which are subsequently used to generate question-answer (QA) pairs for tasks such as object grounding, reasoning, and scene understanding. The dataset includes a variety of scenes from urban and rural India, encompassing highways, service roads, village paths, and congested city streets, captured in both daytime and nighttime settings. Roadscapes has been curated to advance research on visual scene understanding in unstructured environments. In this paper, we describe the data collection and annotation process, present key dataset statistics, and provide initial baselines for image QA tasks using vision-language models.