Pixelwise Traffic Junction Segmentation for Urban Scene Understanding

Self-driving vehicles require detailed information of the surrounding environment to drive autonomously in complex urban scenarios, especially traffic junction crossing. Currently, most self-driving and driver assistance systems depend strongly on GPS and backend high definition map for information about traffic junctions. In this work, we would like to look into the possibility of identifying traffic junctions using onboard cameras by formulating it as a segmentation task. To tackle this, we first analyzed how a junction should be defined in image space, and then used it to extend the Cityscapes dataset with a new Junction class label. We took the extended dataset and trained segmentation models to segment out traffic junction within an image. The models were able to achieve an overall mean Intersection-over-Union mIoU of 73.8% for multi-class semantic segmentation and Intersectionover-Union IoU of 58.7% for Junction. This has the potential to improve self-driving vehicles that depend strongly on a high definition map by providing an alternative source of information for navigation. Finally, we introduced an algorithm operating in sensor space to determine how strong the vehicle should decelerate in order to stop prior to the traffic junction based on the segmentation results.