Driving Important Scene Detection based on user Preferences

Recently, demand has been growing for development data in research on driver assistance systems. However, important scenes in research vary widely from person to person. For example, developers are interested in collision avoidance might be interested in pedestrian darting out or approaching vehicles. On the other hand, some developers may be interested in traffic scenes. Thus, user preferences vary and making the detection of important scenes are complex. We propose a novel approach to detect important scenes based on user's preferences, novelty of a driving scene and driving data. We annotate important scenes from the NuScenes dataset and confirmed improvement in accuracy from existing important scene detection model.