Scene understanding plays a crucial role in autonomous driving by utilizing sensory data for
contextual information extraction and decision making. Beyond modeling advances, the …

As a prerequisite for autonomous driving, scene understanding has attracted extensive
research. With the rise of the convolutional neural network (CNN)-based deep learning …

Deep neural networks are an increasingly important technique for autonomous driving,
especially as a visual perception component. Deployment in a real environment …

Humans assimilate information from the traffic environment mainly through visual
perception. Obviously, the dominant information required to conduct a vehicle can be …

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 …

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 …

Automation is increasingly at the forefront of transportation research, with the potential to
bring fully autonomous vehicles to our roads in the coming years. This comprehensive …

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 …

Accurate scene understanding from multiple sensors mounted on cars is a key requirement
for autonomous driving systems. Nowadays, this task is mainly performed through data …

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 …