Driver Behavior

Research on driver behavior aims to understand and predict human actions behind the wheel to improve road safety and the development of autonomous vehicles. Current studies focus on modeling driver behavior using various techniques, including deep learning architectures like convolutional neural networks, recurrent neural networks (particularly LSTMs and GCNs), and Bayesian methods, often incorporating data from diverse sources such as in-vehicle sensors, dashcams, and roadside cameras. This research is crucial for enhancing advanced driver-assistance systems (ADAS), improving traffic flow management, and ensuring the safe integration of autonomous vehicles into existing road networks. The insights gained are directly applicable to improving traffic safety, designing more effective ADAS features, and creating more realistic simulations for testing autonomous driving systems.