In safety-critical applications such as automated driving, perception errors may create an
imminent risk to vulnerable road users (VRU). To mitigate the occurrence of unexpected and …

This paper outlines the winning solutions employed in addressing the MUAD uncertainty
quantification challenge held at ICCV 2023. The challenge was centered around semantic …

Semantic segmentation of images enables pixel-wise scene understanding which in turn is
a critical component for tasks such as autonomous driving. While recent implementations of …

Decision making in automated driving is highly specific to the environment and thus
semantic segmentation plays a key role in recognizing the objects in the environment …

Deep learning has enabled impressive progress in the accuracy of semantic segmentation.
Yet, the ability to estimate uncertainty and detect anomalies is key for safety-critical …

Semantic segmentation is a critical component in autonomous driving and has to be
thoroughly evaluated due to safety concerns. Deep neural network (DNN) based semantic …

Level 5 autonomy for self-driving cars requires a robust visual perception system that can
parse input images under any visual condition. However, existing semantic segmentation …

Within the context of autonomous driving, safety-related metrics for deep neural networks
have been widely studied for image classification and object detection. In this paper, we …

Semantic segmentation is important for many real-world systems, eg, autonomous vehicles,
which predict the class of each pixel. Recently, deep networks achieved significant progress …

Introduction Semantic segmentation is a crucial visual representation learning task for
autonomous driving systems, as it enables the perception of surrounding objects and road …