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Driving Scene Understanding is a broad field which addresses the problem of recognizing a variety of on-road situations; namely driver behaviour/intention recognition, driver-action causal reasoning, pedestrians' and nearby vehicles' intention recognition, etc. Many existing works propose excellent AI based solutions to these interesting problems by leveraging visual data along with other modalities. However, very few researchers venture into determining the necessary metadata of the visual inputs to their models. This work attempts to put forward some useful insights about the required spatial resolution and temporal context/depth of the visual data for Driving Scene Understanding.