The virtual-to-real paradigm, ie, training models on virtual data and then applying them to
solve real-world problems, has attracted more and more attention from various domains by …

End-to-End driving is a promising paradigm as it circumvents the drawbacks associated with
modular systems, such as their overwhelming complexity and propensity for error …

This work investigates learning pixel-wise semantic image segmentation in urban scenes
without any manual annotation, just from the raw non-curated data collected by cars which …

Deep Neural Networks (DNNs) have primarily been demonstrated to be successful when
large-scale labeled data are available. However, DNNs usually fail when tasked in few …

Pedestrian detection based on deep learning methods makes a big hit during these days.
The key to achieve excellent results for deep learning-based pedestrian detection methods …

To improve their adaptability to various kinds of driving situations, deep learning-based
vision algorithms need images from rare scenes, such as extreme weather conditions and …

For safety purposes, understanding the quality of the lane marks is essential for advanced
driving technologies and pavement road maintenance. In practice, the performance of …

Deep learning algorithms rely heavily on diverse and high-quality datasets to excel in
complex tasks. However, in intricate traffic scenarios, manual data collection often falls short …

Annotated data are essential to the success of training deep neural models for autonomous
driving. Practically, it is both expensive and time consuming to collect and annotate plenty of …

Diverse and large-high-quality data are essential to the deep learning algorithms for
autonomous driving. However, manual data collection in intricate traffic scenarios is …