Capturing uncertainty in object detection is indispensable for safe autonomous driving. In
recent years, deep learning has become the de-facto approach for object detection, and …

Safety assurance of automated driving systems must consider uncertain environment
perception. This paper reviews literature addressing how perception testing is realized as …

For robots to navigate and interact more richly with the world around them, they will likely
require a deeper understanding of the world in which they operate. In robotics and related …

The inherent ambiguity in ground-truth annotations of 3D bounding boxes, caused by
occlusions, signal missing, or manual annotation errors, can confuse deep 3D object …

Deployed into an open world, object detectors are prone to open-set errors, false positive
detections of object classes not present in the training dataset. We propose GMM-Det, a real …

Object detection is a classical problem in computer vision, and the vast majority of
approaches require large annotated datasets for training and evaluation purposes. The most …

In this paper, we propose a novel form of the loss function to increase the performance of
LiDAR-based 3D object detection and obtain more explainable and convincing uncertainty …

Purpose The paper aims to address the tracking algorithm based on deep learning and four
deep learning tracking models developed. They compared with each other to prevent …

Safety is critical for autonomous driving, and one aspect of improving safety is to accurately
capture the uncertainties of the perception system, especially knowing the unknown …

In state-of-the-art deep learning for object recognition, Softmax and Sigmoid layers are most
commonly employed as the predictor outputs. Such layers often produce overconfidence …