The field of intelligent transportation systems is moving towards more advanced and integrated solutions, with a focus on improving road safety, traffic efficiency, and driver behavior modeling. Recent developments have highlighted the importance of multimodal data fusion, interaction-aware systems, and demand-driven application management. Notably, innovative approaches such as privacy-preserving data collection, social spatio-temporal graph convolutional neural networks, and multi-scale feature interaction networks have shown promising results in addressing complex challenges in the field.

Noteworthy papers include: V-SenseDrive, which presents a privacy-preserving multimodal driver behavior dataset collected in Pakistan, filling a critical gap in the global landscape of driver behavior datasets. MsFIN, which proposes a multi-scale feature interaction network for early-stage accident anticipation from dashcam videos, significantly outperforming state-of-the-art models in prediction correctness and earliness.