Driver Identification

Driver identification research focuses on automatically recognizing drivers based on their unique driving behaviors, aiming to improve transportation safety and security, and enable personalized services. Current approaches leverage machine learning, employing techniques like temporal convolutional networks and Siamese networks to analyze diverse data sources, including vehicle sensor data (e.g., CAN-BUS) and GPS trajectories, to extract characteristic driving patterns. This field is significant for applications ranging from fraud detection in ride-sharing services to enhancing vehicle security and personalized in-car experiences, with ongoing research striving to improve accuracy and robustness across varied driving styles and vehicle types.