Estimating the uncertainty of a neural network plays a fundamental role in safety-critical
settings. In perception for autonomous driving, measuring the uncertainty means providing …

Object detection algorithms are essential components for perceiving the environment in
safety-critical systems like automated driving. However, current state-of-the-art algorithms …

The capability to detect objects is a core part of autonomous driving. Due to sensor noise
and incomplete data, perfectly detecting and localizing every object is infeasible. Therefore …

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 …

Uncertainty estimation is crucial in safety-critical settings such as automated driving as it
provides valuable information for several downstream tasks including high-level decision …

There has been a recent emergence of sampling-based techniques for estimating epistemic
uncertainty in deep neural networks. While these methods can be applied to classification or …

Probabilistic object detection is the task of detecting objects in images and accurately
quantifying the spatial and semantic uncertainties of the detection. Measuring uncertainty is …

When incorporating deep neural networks into robotic systems, a major challenge is the lack
of uncertainty measures associated with their output predictions. Methods for uncertainty …

Object detection in autonomous cars is commonly based on camera images and Lidar
inputs, which are often used to train prediction models such as deep artificial neural …

Abstract The Probability Object Detection (POD) aims to measure spatial and label
uncertainty of an object detector. The uncertainty measurement is important in robotic …