DataSet for Unflappable Driving : UNFLAPSet

Abstract Autonomous vehicles must be aware of dynamic and static objects, road lanes, road signs and road markings. Recent autonomous vehicles awareness studies adaptable to various road environments continues, it is necessary to construct datasets that accurately reflect the real driving environment. The existing datasets consist of annotations that focus on dynamic and static objects, lanes, and road signs in the driving environment. These annotations enable management of object distance and avoidance, lane recognition and maintenance and awareness of road signs. Although road markings on the road surface provide the traffic regulations and guidance for driving lanes in the driving environment, there is a lack of the road marking datasets containing various types of directions and regulations. When driving without recognizing road markings, difficulty in recognizing lane information poses challenges in determining the appropriate lanes for the driving route and the limitation in predicting the movement of surrounding cars presents difficulties in maintaining stable driving responses. This paper presents a road marking dataset, UNFLAPSet(UNFLAPpable Set). UNFLAPSet is able to be aware of multidimensional information by including the various types of direction and regulations compared to existing road marking datasets. UNFLAPSet consists of three classes - 1. Driving Direction Centric class(DDCclass) 2. Capable of Lane Maneuver class(CLMclass) 3. Specific Condition Caution class(SCCclass), which are based on the meaning of road markings for emphasizing the primary implication of labels unlike the existing road marking datasets. Especially, Merge Line, Merge Arrow and Uturn Dot Line of CLMclass enable the prediction of surrounding vehicles’ movement and subsequently allow for stable responses. Furthermore, restricted direction road markings of SCCclass helps mitigate the risk of accidents caused by driving in unsuitable lanes, thereby facilitating the maintenance of smooth traffic flow. The validation results of UNFLAPSet showed high recognition accuracy for each label, enabling predictable driving integrated perception of driving lanes and surrounding lanes based on road surface marking recognition using UNFLAPSet.