Semantic segmentation using deep neural networks has been widely explored to generate
high-level contextual information for autonomous vehicles. To acquire a complete 180° …

With the availability of many datasets tailored for autonomous driving in real-world urban
scenes, semantic segmentation for urban driving scenes achieves significant progress …

Visible camera-based semantic segmentation and semantic forecasting are important
perception tasks in autonomous driving. In semantic segmentation, the current frame's pixel …

This paper proposes an improved Deeplabv3+ model for semantic segmentation of urban
scenes targeting autonomous driving applications. A high-quality semantic segmentation …

We are exploring the use of semantic scene understanding in autonomous navigation for the
Tsukuba Challenge. However, manually creating a comprehensive dataset that covers …

Semantic segmentation is a challenging task that addresses most of the perception needs of
intelligent vehicles (IVs) in an unified way. Deep neural networks excel at this task, as they …

Fast semantic image segmentation is crucial for autonomous systems, as it allows an
autonomous system (eg, self-driving car, drone, etc.) to interpret its environment on-the-fly …

Semantic scene understanding is a useful capability for autonomous vehicles operating in
off-roads. While cameras are the most common sensor used for semantic classification, the …

Deep neural networks have been frequently used for semantic scene understanding in
recent years. Effective and robust segmentation in outdoor scene is prerequisite for safe …

Semantic segmentation aims at assigning labels to every pixel of a given image. In the
context of autonomous vehicles, semantic segmentation models should be trained with data …