In current decades, significant advancements in robotics engineering and autonomous
vehicles have improved the requirement for precise depth measurements. Depth estimation …

Current artificial neural networks mainly conduct the learning process in the spatial domain
but neglect the frequency domain learning. However, the learning course performed in the …

Scene perception is essential for driving decision-making and traffic safety. However, fog, as
a kind of common weather, frequently appears in the real world, especially in mountain …

Self-supervised monocular depth estimation has attracted significant attention in computer
vision, especially for applications like autonomous driving and robotics. Recently, CNNs and …

Monocular depth estimation plays a crucial role in various computer vision and robotics
applications, particularly in self-supervised methods that do not require ground-truth depth …

Driver emotion is considered an essential factor associated with driving behaviors and thus
influences traffic safety. Dynamically and accurately recognizing the emotions of drivers …

3D object detection by autonomous vehicles is integral to intelligent transportation. Existing
systems often compromise essential foreground point features and local spatial interactions …

Cutting-edge connected vehicle (CV) technologies have drawn much attention in recent
years. The real-time traffic data captured by a CV can be shared with other CVs and data …

We present CoopNet, an approach that improves the cooperation of co-trained networks by
dynamically adapting the apportionment of gradient, to ensure equitable learning progress …

Monocular depth estimation aims to infer a depth map from a single image. Although
supervised learning-based methods have achieved remarkable performance, they generally …