  Recent development in autonomous driving involves high-level computer vision
and detailed road scene understanding. Today, most autonomous vehicles are
using mediated perception approach for path planning and control, which highly
rely on high-definition 3D maps and real time sensors. Recent research efforts
aim to substitute the massive HD maps with coarse road attributes. In this
paper, we follow the direct perception based method to train a deep neural
network for affordance learning in autonomous driving. Our goal in this work is
to develop the affordance learning model based on freely available Google
Street View panoramas and Open Street Map road vector attributes. Driving scene
understanding can be achieved by learning affordances from the images captured
by car-mounted cameras. Such scene understanding by learning affordances may be
useful for corroborating base maps such as HD maps so that the required data
storage space is minimized and available for processing in real time. We
compare capability in road attribute identification between human volunteers
and our model by experimental evaluation. Our results indicate that this method
could act as a cheaper way for training data collection in autonomous driving.
The cross validation results also indicate the effectiveness of our model.
