Enhancing Road Safety Through Accurate Detection of Hazardous Driving Behaviors With Graph Convolutional Recurrent Networks

Car accidents remain a significant public safety issue worldwide, with the majority of them attributed to driver errors stemming from inadequate driving knowledge, non-compliance with regulations, and poor driving habits. To increase road safety, several studies proposed Driving Behavior Detection (DBD) systems that can differentiate between safe and unsafe driving behavior. Many of these papers used the sensor information retrieved from the CAN (Controller Area Network) bus to construct their models. According to the existing literature, using public sensors reduces the detection model's accuracy while adding vendor-specific sensors into the data increases the accuracy. However, the earlier techniques' utility is limited by the use of non-public sensors. As a result, this paper presents a reliable DBD system based on Graph Convolutional Long Short-Term Memory networks in order to improve the detection model's precision and practical usability for public sensors. Additionally, non-public sensors were utilized to assess the model's effectiveness. The proposed model achieved an accuracy of 97.5% for public sensors and an average accuracy of 98.1% for non-public sensors, which shows that the proposed model can produce consistent and accurate results for both scenarios. The proposed DBD system deployed on Raspberry Pi at the network edge to analyze the driver's driving behavior locally. Drivers can access daily driving condition reports, sensor data, and prediction results from the DBD system through the monitoring dashboard. A voice warning from the dashboard also warns drivers of hazardous driving conditions.