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 …

Predictive uncertainty estimation is an essential next step for the reliable deployment of
deep object detectors in safety-critical tasks. In this work, we focus on estimating predictive …

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 …

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

For self-driving cars that operate based on battery-generated power, detection and control
are commonly performed in embedded systems to reduce power consumption. To drive …

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 …

Object detection is arguably one of the most important and complex tasks to enable the
advent of next-generation autonomous systems. Recent advancements in deep learning …

There are a huge number of features which are said to improve Convolutional Neural
Network (CNN) accuracy. Practical testing of combinations of such features on large …

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 …

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 …