Driver Behavior Monitoring and Warning With Dangerous Driving Detection Based on the Internet of Vehicles

In this paper, we design a driver behavior monitoring and warning (DBMW) framework to detect dangerous driving for enhancing road safety through the Internet of Vehicles (IoV). The designed DBMW framework applies onboard image sensors and wearable devices to detect the deviation degree of vehicles and trace the head motion of drivers, respectively. According to our review of relevant research, DBMW is the first framework for driver behavior monitoring and warning that provides the following features: 1) DBMW can keep recognizing the located lane lines and estimating the power spectral density of lane deviation for a vehicle through image sensors, 2) DBMW can keep monitoring driver behaviors and measuring the anomaly level of a driver through wearable devices, and 3) DBMW can instantly send the warning messages of potential dangerous driving to neighboring vehicles and nearby pedestrians through IoV communications as necessary, which makes vehicles and pedestrians be aware of the existence of surrounding dangerous drivers in advance to keep alerting and avoid potential accidents/collisions. In particular, the prototype consisting of an Android-based sensing unit and an Arduino-based wearable device is implemented to verify the feasibility and superiority of DBMW. Experimental results show that DBMW outperforms existing methods and can significantly improve the detection accuracy and false alarm rates of dangerous driving behaviors.