3 accepted papers
Currently, many change detection (CD) methods rely on supervised learning, which necessitates extensive manually annotated data, resulting in significant labor and time requirements. Recently, semi-supervised (SS) approaches have emerged in the CD community, which exploit large amounts of unlabeled…
Cooperatively utilizing both ego-vehicle and infrastructure sensor data can significantly enhance autonomous driving perception abilities. However, the uncertain temporal asynchrony and limited communication conditions that are present in traffic environments can lead to fusion misalignment and cons…
Utilizing infrastructure and vehicle-side information to track and forecast the behaviors of surrounding traffic participants can significantly improve decision-making and safety in autonomous driving. However, the lack of real-world sequential datasets limits research in this area. To address this…