Proximity based automatic data annotation for autonomous driving

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.