As technology continues to develop, computer vision (CV) applications are becoming
increasingly widespread in the intelligent transportation systems (ITS) context. These …

This review comprehensively investigates the current state and emerging trends of
autonomous vehicle terrain detection and segmentation. By systematically reviewing …

Efficient data utilization is crucial for advancing 3D scene understanding in autonomous
driving, where reliance on heavily human-annotated LiDAR point clouds challenges fully …

Semantic segmentation benchmarks in the realm of autonomous driving are dominated by
large pre-trained transformers, yet their widespread adoption is impeded by substantial …

This paper explores the emerging knowledge-driven autonomous driving technologies. Our
investigation highlights the limitations of current autonomous driving systems, in particular …

Continual learning, also known as incremental learning or life-long learning, stands at the
forefront of deep learning and AI systems. It breaks through the obstacle of one-way training …

Image segmentation plays a critical role in autonomous driving by providing vehicles with a
detailed and accurate understanding of their surroundings. Transformers have recently …

Fusing an arbitrary number of modalities is vital for achieving robust multi-modal fusion of
semantic segmentation yet remains less explored to date. Recent endeavors regard RGB …

Background Efficient and site-specific weed management is a critical step in many
agricultural tasks. Image captures from drones and modern machine learning based …

Deep learning-based Autonomous Driving (AD) models often exhibit poor generalization
due to data heterogeneity in an ever domain-shifting environment. While Federated …