  The use of object detection algorithms is becoming increasingly important in
autonomous vehicles, and object detection at high accuracy and a fast inference
speed is essential for safe autonomous driving. A false positive (FP) from a
false localization during autonomous driving can lead to fatal accidents and
hinder safe and efficient driving. Therefore, a detection algorithm that can
cope with mislocalizations is required in autonomous driving applications. This
paper proposes a method for improving the detection accuracy while supporting a
real-time operation by modeling the bounding box (bbox) of YOLOv3, which is the
most representative of one-stage detectors, with a Gaussian parameter and
redesigning the loss function. In addition, this paper proposes a method for
predicting the localization uncertainty that indicates the reliability of bbox.
By using the predicted localization uncertainty during the detection process,
the proposed schemes can significantly reduce the FP and increase the true
positive (TP), thereby improving the accuracy. Compared to a conventional
YOLOv3, the proposed algorithm, Gaussian YOLOv3, improves the mean average
precision (mAP) by 3.09 and 3.5 on the KITTI and Berkeley deep drive (BDD)
datasets, respectively. Nevertheless, the proposed algorithm is capable of
real-time detection at faster than 42 frames per second (fps) and shows a
higher accuracy than previous approaches with a similar fps. Therefore, the
proposed algorithm is the most suitable for autonomous driving applications.
