Traffic Scene

Traffic scene understanding aims to comprehensively model and predict the complex interactions within dynamic road environments, crucial for autonomous driving and intelligent transportation systems. Current research heavily focuses on developing robust perception models using deep learning architectures like Graph Neural Networks and transformers, often incorporating multi-modal data (RGB, depth, LiDAR) and leveraging techniques such as contrastive learning and self-supervised pretraining to improve accuracy and generalization. These advancements are driving progress in tasks such as object detection, motion prediction, and scene understanding, ultimately contributing to safer and more efficient transportation systems.