LAUSR

The recent development in autonomous driving involves high-level computer vision and detailed road scene understanding. Today, most autonomous vehicles employ expensive high quality sensor-set such as light detection and ranging ( LIDAR ) and HD maps with high level annotations. In this paper, we propose a scalable and affordable data collection and annotation framework, image-to-map annotation proximity ( I2MAP ), for affordance learning in autonomous driving applications. We provide a new driving dataset using our proposed framework for driving scene affordance learning by calibrating the data samples with available tags from online database such as open street map ( OSM ) . Our benchmark consists of 40 000 images with more than 40 affordance labels under various day time and weather even with very challenging heavy snow. We implemented sample advanced driver-assistance systems ( ADAS ) functions by training our data with neural networks ( NN ) and cross-validate the results on benchmarks like KITTI and BDD100K, which indicate the effectiveness of our framework and training models.