Driving Behavior

Research on driving behavior focuses on automatically identifying and classifying various driving actions, both normal and anomalous, to improve road safety and autonomous vehicle performance. Current efforts leverage machine learning, particularly deep learning architectures like convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and vision transformers, often incorporating surrogate safety measures and semi-supervised learning techniques to address data limitations. These models analyze diverse data sources, including vehicle trajectory data, video recordings, and sensor measurements, to detect behaviors ranging from subtle cyberattacks to distracted driving and cognitive impairment indicators. The resulting advancements have significant implications for improving traffic safety, developing more robust autonomous driving systems, and enhancing driver assistance technologies.