Software-Defined Vehicular Networks (SDVNs) revolutionize modern transportation by enabling dynamic and adaptable communication infrastructures. However, accurately capturing the dynamic communication patterns in vehicular networks, characterized by intricate spatio-temporal dynamics, remains a challenge with traditional graph-based models. Hypergraphs, due to their ability to represent multi-way relationships, provide a more nuanced representation of these dynamics. Building on this hypergraph foundation, we introduce a novel hypergraph-based routing algorithm. We jointly train a model that incorporates Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU) using a Deep Deterministic Policy Gradient (DDPG) approach. This model carefully extracts spatial and temporal traffic matrices, capturing elements such as location, time, velocity, inter-dependencies, and distance. An integrated attention mechanism refines these matrices, ensuring precision in capturing vehicular dynamics. The culmination of these components results in routing decisions that are both responsive and anticipatory. Through detailed empirical experiments using a testbed, simulations with OMNeT++, and theoretical assessments grounded in real-world datasets, we demonstrate the distinct advantages of our methodology. Furthermore, when benchmarked against existing solutions, our technique performs better in model interpretability, delay minimization, rapid convergence, reducing complexity, and minimizing memory footprint.

Improving driving safety by monitoring driver behavior is an excellent example of Advanced Driver Assistance Systems (ADAS). This paper proposes an end-to-end transformer-based driver behavior classification framework named Trans-DBC. It calculates driver behavior from the multivariate time-series smartphone telematics data by learning short and long-range temporal dependencies effectively and accurately, unlike prior data-driven deep learning models that capture information locally via convolutional or recurrent structure in an iterative manner. The extensive human-in-the-loop study on a publicly available UAH-DriveSet dataset shows that the proposed technique can classify unsafe driving behavior with a 96% of average weighted precision, recall, F-measure, and 95.38% accuracy. Our suggested model outperforms baselines and state-of-the-art models on the UAH-DriveSet dataset and five multivariate time-series datasets in driving behavior analysis.