Recognition Assistant Framework Based on Deep Learning for Autonomous Driving : Restoring Damaged Road Sign Information
Unpredictable situations frequently occur in real driving environments, and it is often difficult to recognize road signs. In this case, autonomous vehicles (AVs) have a limited ability to predict areas that cannot be seen or detected, making it difficult to judge objects accurately when some information is lost. Therefore, this study proposes a framework that helps AVs infer proper information under limited conditions. In particular, the proposed framework allows AVs to restore insufficient information on the signs and reflect them in the driving mode when they encounter damaged or obscured road signs while driving. The entire process consists of three steps. First, the missing part of the road sign is restored using the pretrained iGPT model. Next, the sample image with the highest classification accuracy and restored quality is selected among several restored sample images. Finally, the selected image is provided to the user through the designed graphical user interface (GUI), and the restored road sign information is applied to the driving context. The proposed framework measures the recognition accuracy for two datasets, the European Traffic Sign Dataset (ETSD) and German Traffic Sign Recognition Benchmark (GTSRB). As a result, the accuracy after restoration improved by an average of 72.52 and 79.43 percentage points, respectively, compared with the unrestored accuracy. This outcome indicates the possibility of its use as an autonomous driving assistance system for safe driving. In addition, the user experiments confirmed that whether the framework was applied or not, it was helpful to positively improve the user experience for AVs
Year of publication: |
[2022]
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Authors: | Park, Jeongeun ; Lee, Kisu ; Kim, Ha Young |
Publisher: |
[S.l.] : SSRN |
Saved in:
freely available
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