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 …

Environmental perception is an important aspect within the field of autonomous vehicles that
provides crucial information about the driving domain, including but not limited to identifying …

Convolutional Neural Networks (CNNs) are successfully used for the important automotive
visual perception tasks including object recognition, motion and depth estimation, visual …

Semantic segmentation is a critical component for street understanding task in autonomous
driving field. Existing various methods either focus on constructing the object's inner …

Semantic segmentation is key in autonomous driving. Using deep visual learning
architectures is not trivial in this context, because of the challenges in creating suitable large …

The tradeoff between speed and accuracy is important in semantic segmentation problems,
especially for resource-constrained platforms, such as intelligent vehicles. In this paper, we …

Semantic segmentation is an important research topic in the environment perception of
intelligent vehicles. Many semantic segmentation networks based on bilateral architecture …

In this paper, an application of semantic image segmentation is implemented in order to
support autonomous driving of autonomous vehicles using deep learning based methods …

Semantic image segmentation can be used in various driving applications, such as
automatic braking, road sign alerts, park assists, and pedestrian warnings. More often, AI …

We focus on the very challenging task of semantic segmentation for autonomous driving
system. It must deliver decent semantic segmentation result for traffic critical objects real …