Edge AI-Assisted IoV Application for Aggressive Driver Monitoring: A Case Study on Public Transport Buses

Abstract

With increasing adoption of digital technologies to automotive industry, the revo-lution of the vehicles opens new doors for many advanced applications to improve the driver safety and comfort. Thanks to Advanced Driver Assistance Systems (ADAS), no doubt that the future driving experience will be safer than today. De-spite the emergence of new trends, road accidents caused by aggressive driving are still a major problem in many countries. This study presents an edge AI-assisted ag-gressive driver monitoring system based on Internet of Vehicles (IoV) model. In the proposed system, the kNN algorithm and dynamic time warping method are used to recognize the signal patterns of aggressive drivers. The hardware platform is built on the RP2040 microcontroller-based Raspberry Pi Pico board and the Waveshare Quad Expander used for sensor extensions. The MPU-9250 9-axis motion tracking sensor is used as an inertial measurement unit (IMU) to identify the patterns of driv-ers who did sudden lane changes, heavy acceleration, and harsh braking on the roads. Besides, the required software is created using the MicroPython scripting language via Thonny IDE. The proposed method is tested on public transport vehi-cles to determine the drivers engaging in dangerous driving behavior for passengers. The obtained results show that the proposed method can provide satisfactory success to support for recognizing the aggressive behavior of drivers.