  Text and signs around roads provide crucial information for drivers, vital
for safe navigation and situational awareness. Scene text recognition in motion
is a challenging problem, while textual cues typically appear for a short time
span, and early detection at a distance is necessary. Systems that exploit such
information to assist the driver should not only extract and incorporate visual
and textual cues from the video stream but also reason over time. To address
this issue, we introduce RoadTextVQA, a new dataset for the task of video
question answering (VideoQA) in the context of driver assistance. RoadTextVQA
consists of $3,222$ driving videos collected from multiple countries, annotated
with $10,500$ questions, all based on text or road signs present in the driving
videos. We assess the performance of state-of-the-art video question answering
models on our RoadTextVQA dataset, highlighting the significant potential for
improvement in this domain and the usefulness of the dataset in advancing
research on in-vehicle support systems and text-aware multimodal question
answering. The dataset is available at
http://cvit.iiit.ac.in/research/projects/cvit-projects/roadtextvqa
