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

Abstract

With the increasing adoption of digital technologies in the automotive industry, the revolution of vehicles opens new doors for many advanced applications to improve driver safety and comfort. Thanks to Advanced Driver Assistance Systems (ADAS), the future driving experience will undoubtedly be safer than today. But, despite the emergence of new trends, road accidents caused by aggressive driving are still a significant problem in many countries. This study presents an edge Al-assisted aggressive driver monitoring system based on the 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 drivers who make sudden lane changes, heavy acceleration, and harsh braking on the roads. Besides, the required software is created using the MicroPy-thon scripting language via Thonny IDE. The proposed method is tested on public transport vehicles to determine the drivers engaging in dangerous driving behavior for passengers. The obtained results show that the proposed method can provide satisfactory success in supporting the recognition of the aggressive behavior of drivers.