Autonomous driving has rapidly developed and shown promising performance with recent
advances in hardware and deep learning methods. High-quality datasets are fundamental …

Autonomous driving has rapidly developed and shown promising performance due to recent
advances in hardware and deep learning techniques. High-quality datasets are fundamental …

Reliable segmentation of road lines and markings is critical to autonomous driving. Our work
is motivated by the observations that road lines and markings are (1) frequently occluded in …

Artificial Intelligence has been widely applied in intelligent transportation systems. In this
work, Swin-APT, a deep learning-based approach for semantic segmentation and object …

With the rapid development of the autonomous driving industry, there is increasing research
on related perception tasks. However, research on road surface traffic sign detection tasks is …

Pedestrian crossings are an essential part of the urban landscape, providing safe passage
for pedestrians to cross busy streets. While some are regulated by timed signals and are …

Accurate detection of pavement markings at the pixel level is crucial for enhancing traffic
safety. The majority of current advanced deep-learning networks predominantly focus on …

It is a challenging task to accurately segment damaged road markings from images, mainly
due to their fragmented, dense, small-scale, and blurry nature. This study proposes a multi …

Road surface markings, like symbols and line markings, are vital traffic infrastructures for
driving safety and efficiency. However, real-world conditions can impair the utility of existing …

3D object detection is crucial for autonomous driving. However, most existing methods focus
on the foreground objects, such as vehicles and pedestrians, while ignoring some important …