Pedestrian trajectory prediction is one of the main concerns of computer vision problems in
the automotive industry, especially in the field of advanced driver assistance systems. The …

Understanding urban scenes is a fundamental capability required of intelligent vehicles.
Depth cues provide useful geometric information for semantic segmentation, thus …

Semantic segmentation of urban scenes is an essential component in various applications
of autonomous driving. It makes great progress with the rise of deep learning technologies …

Joint detection of drivable areas and road anomalies is very important for mobile robots.
Recently, many semantic segmentation approaches based on convolutional neural …

An accurate vehicle trajectory prediction promotes understanding of the traffic environment
and enables task criticality assessment in advanced driver assistance systems (ADASs) in …

Understanding the surrounding environment is crucial for autonomous vehicles to make
correct driving decisions. In particular, urban scene segmentation is a significant integral …

Most existing studies performed video denoising by using multiple adjacent noisy frames to
recover one clean frame; however, despite achieving relatively good quality for each …

In recent years, the advancement of deep-learning technologies has greatly promoted the
research progress of autonomous driving. However, deep neural network is like a black box …

In this study, a semantic segmentation network is presented to develop an indoor navigation
system for a mobile robot. Semantic segmentation can be applied by adopting different …

In recent years, the application of deep learning to the inertial navigation field has brought
new vitality to inertial navigation technology. In this study, we propose a method using long …