  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 improve
road safety, Driving Behavior Detection (DBD) systems have been proposed in
several studies to identify safe and unsafe driving behavior. Many of these
studies have utilized sensor data obtained from the Controller Area Network
(CAN) bus to construct their models. However, the use of publicly available
sensors is known to reduce the accuracy of detection models, while
incorporating vendor-specific sensors into the dataset increases accuracy. To
address the limitations of existing approaches, we present a reliable DBD
system based on Graph Convolutional Long Short-Term Memory Networks (GConvLSTM)
that enhances the precision and practicality of DBD models using public
sensors. Additionally, we incorporate non-public sensors to evaluate the
model's effectiveness. Our proposed model achieved a high accuracy of 97.5\%
for public sensors and an average accuracy of 98.1\% for non-public sensors,
indicating its consistency and accuracy in both settings. To enable local
driver behavior analysis, we deployed our DBD system on a Raspberry Pi at the
network edge, with drivers able to access daily driving condition reports,
sensor data, and prediction results through a monitoring dashboard.
Furthermore, the dashboard issues voice warnings to alert drivers of hazardous
driving conditions. Our findings demonstrate that the proposed system can
effectively detect hazardous and unsafe driving behavior, with potential
applications in improving road safety and reducing the number of accidents
caused by driver errors.
