This research explores the design of a system for monitoring driver drowsiness and supervising seat belt usage in interprovincial buses. In Peru, road accidents involving long-distance bus transportation amounted to 5449 in 2022, and the human factor plays a significant role. It is essential to understand how the use of non-invasive sensors for monitoring and supervising passengers and drivers can enhance safety in interprovincial transportation. The objective of this research is to develop a system using a Raspberry Pi 4 and Arduino Nano that allows for the storage of monitoring data. To achieve this, a conventional camera and MediaPipe were used for driver drowsiness detection, while passenger supervision was carried out using a combination of commercially available sensors as well as custom-built sensors. RS485 communication was utilized to store data related to both the driver and passengers. The simulations conducted demonstrate a high level of reliability in detecting driver drowsiness under specific conditions and the correct operation of the sensors for passenger supervision. Therefore, the proposed system is feasible and can be implemented for real-world testing. The implications of this research suggest that the system’s cost is not a barrier to its implementation, thus contributing to improved safety in interprovincial transportation.

This research explores the possibility of designing a system for monitoring driver drowsiness and supervising the use of seat belts in buses. Road accidents in long-distance bus transport in 2022 amount to 5449 only at the national level, where the human factor plays an important role. It is necessary to understand how the use of non-invasive sensors on passengers and drivers for monitoring and supervision can improve safety in interprovincial transportation. The objective of this research is to realize a system with the use of a Raspberry Pi 4 and Arduino NANO, which allows us to store such data. To achieve this, we used a conventional camera and MediaPipe to monitor driver drowsiness, while for passenger monitoring, we used different sensors available on the market and our own. RS485 communication was used for driver and passenger data storage. The simulations performed show a high-reliability rate in the detection of driver drowsiness under specific conditions and the correct functioning of the sensors for passenger monitoring. Therefore, it is possible to conclude the feasibility of the proposed system and that it can be implemented for real tests. The implications of this research indicate that the cost of the system is not an impediment to its implementation and thus improves safety in interprovincial transportation.