Abstract
Detecting failure cases is an essential element for ensuring the safety self-driving system. Any fault in the system directly leads to an accident. In this paper, we analyze the failure of semantic segmentation, which is crucial for autonomous driving system, and detect the failure cases of the predicted segmentation map by predicting mean intersection of union (mIoU). Furthermore, we design a deep neural network for predicting mIoU of segmentation map without the ground truth and introduce a new loss function for training imbalance data. The proposed method not only predicts the mIoU, but also detects failure cases using the predicted mIoU value. The experimental results on Cityscapes data show our network gives prediction accuracy of 93.21% and failure detection accuracy of 84.8%. It also performs well on a challenging dataset generated from the vertical vehicle camera of the Hyundai Motor Group with 90.51% mIoU prediction accuracy and 83.33% failure detection accuracy.
Highlights
In recent years, with deep learning breakthroughs on vision applications [1,2,3,4], autonomous driving vehicle technology has been commercialized
We conducted an experiment to ensure that performance is maintained even when using GT mean intersection of union (mIoU) values generated by other segmentation models after fixing the structure of the network, which is the second step of our network structure
0.6 was set as the threshold value of the mIoU corresponding to the failure case, where all performance indicators are generally good as following Table 17
Summary
With deep learning breakthroughs on vision applications [1,2,3,4], autonomous driving vehicle technology has been commercialized. The semantic segmentation task [20,21,22,23] is indispensable in autonomous driving systems which gives detection and identification information of objects. Based on vision methods, advanced driver assistance systems make autonomous driving possible. The National Highway Traffic Safety Administration [24] categorizes five levels of developmental stages of autonomous driving technology: .
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