Driving Style Recognition for Intelligent Vehicle using Unsupervised Clustering Algorithms - Abhishek Dixit (International Journal of Intelligent Systems and Applications in Engineering, 2024)
The manner in which a driver operates their vehicles has a significant impact on both energy management and driving safety. Moreover, it is a crucial factor in the advancement of driver assistance systems (ADAS), which aim to increase the level of vehicle automation. As a result, numerous research and development initiatives have been undertaken to identify and classify driving styles. In this study, we have used principal component analysis for feature reduction and K means clustering algorithm for driving style identification of the vehicle. To evaluate the performance of the proposed approach, it was tested using vehicle trajectory data from the Next Generation Simulation (NGSIM) project, specifically the datasets collected on US Highway 101 and I-80. The proposed approach introduces a novel method that enhances efficiency and accuracy, offering a significant advancement in addressing complex challenges within its respective domain.