Detection of driving actions on steering wheel using triboelectric nanogenerator via machine learning

The sensor-based early recognition of driver's driving actions on steering wheels is a complementary means of Intelligent Driver Assistance Systems (IDAS), which can help prevent traffic accidents. In this paper, using triboelectric nanogenerator (TENG) as sensor for detecting driver's steering actions is studied. Two driving simulator based experiments are designed and conducted. First, response speed of three sensors (driving simulator, camera and TENG) is quantified and compared. Then, a machine learning algorithm is designed and trained to detect three types of steering actions. Using this algorithm, electrical signals from TENGs can be used to detect driver's steering actions. Our results show that TENG has the fastest response speed statistically. The trained algorithm has an accuracy of 92.0% in test dataset. This study may demonstrate the potential in using TENG as sensor for driver's steering action detection.