Auditory and visual warning information generation of the risk object in driving scenes based on weakly supervised learning
Yingjie Niu, Ming Ding, Yuxiao Zhang, Kento Ohtani, Kazuya Takeda · 2022 IEEE Intelligent Vehicles Symposium (IV) · 2022
In this research, a two-stage risk object warning method is proposed to generate the auditory and visual warning information simultaneously from the driving scene. The auditory warning module (AWM) is designed as a classification task by combining the rough location and type information as warning sentences and treating each sentence as one class. The visual warning module (VWM) is designed as a weakly supervised method to save the labor-intensive bounding box marking of risk objects. To confirm the effectiveness of the proposed method, we also create a linguistic risk notification (LRN) dataset by describing the driving scenario as several different sentences. The average accuracy of auditory warning is 96.4% for generating the warning sentences. The average accuracy of the weakly supervised visual warning algorithm is 81.3% for getting the risk vehicle localization without any supervisory information.