A Survey on Driver Behavior Detection-Based Internet of Vehicle: Issues, Challenges, Motivation and Recommendations

Numerous academic studies and government publications concur that careless driving behaviors on the road are the primary contributor to accidents. Therefore, studying driver behavior is seen as being of great use in the design and development of Intelligent Transportation Systems (ITS). The rising number of traffic accidents is ascribed to irresponsible driving, inadequate preventative measures, as well as dependence on sluggish and ineffective support systems. There are multiple smartphone applications available to help drivers become better drivers. Recently, vehicles have gained access to the computational capacity of cloud servers to run machine learning (ML) algorithms owing to mobile cloud computing. In order to address issues that have not yet been addressed by previous studies, this study intends to analyze and carefully examine the literature in the driver behavior-based Internet of Vehicles (IoV) domain. Thus, the development of intelligent transportation systems' crucial behavior analysis is covered. Sensors utilized in investigations, the impact of thresholds on labelling procedures, data balance and classification accuracy, and the thresholds in recognizing driving styles are among the topics covered in the examined studies.