A Review of Non-Lane Road Marking Detection and Recognition

Environment perception is a critical function used by driving automation systems, or self-driving cars, for detecting objects such as obstacles, lane markings, and road signs. In order to replace human drivers, self-driving cars will need to safely operate in parking lots, private roads, underground, or any other environment with poor GPS signals or uncharted infrastructure. While much attention has been spent on recognizing lane markings, non-lane road markings have received considerably less attention. Current perception systems can recognize only a small subset of markings and often only under favorable weather conditions. This limitation is exacerbated by the current quality of scene segmentation data sets. Only a select few of existing data sets have annotations for non-lane road markings, and the ones that do only have them for a small number of marking types. Additionally most of the data sets were generated under one type of driving condition. Finally, it is difficult to determine if current recognition systems can satisfy real-time requirements. This paper investigates the current limitations and challenges for non-lane road marking detection and recognition including recognition capabilities, data set quality, and inference times.