Multimodal fusion can make semantic segmentation more robust. However, fusing an
arbitrary number of modalities remains underexplored. To delve into this problem, we create …

Moving object detection is a critical task for autonomous vehicles. As dynamic objects
represent higher collision risk than static ones, our own ego-trajectories have to be planned …

LiDAR has become a standard sensor for autonomous driving applications as they provide
highly precise 3D point clouds. LiDAR is also robust for low-light scenarios at night-time or …

Multi-task learning is commonly used in autonomous driving for solving various visual
perception tasks. It offers significant benefits in terms of both performance and computational …

Whole understanding of the surroundings is paramount to autonomous systems. Recent
works have shown that deep neural networks can learn geometry (depth) and motion …

Ensuring the safety of all traffic participants is a prerequisite for bringing intelligent vehicles
closer to practical applications. The assistance system should not only achieve high …

Unsupervised optical flow estimation is especially hard near occlusions and motion
boundaries and in low-texture regions. We show that additional information such as …

The robustness of semantic segmentation on edge cases of traffic scene is a vital factor for
the safety of intelligent transportation. However, most of the critical scenes of traffic accidents …

Accurately segmenting contrast-filled vessels from X-ray coronary angiography (XCA) image
sequence is an essential step for the diagnosis and therapy of coronary artery disease …

Motion and depth provide critical information in autonomous driving and they are commonly
used for generic object detection. In this paper, we leverage them for improving semantic …