An Effective YOLO-Based Proactive Blind Spot Warning System for Motorcycles

Abstract

Interest in advanced driver assistance systems (ADAS) is booming in recent years. One of the most effervescent ADAS features is blind spot detection (BSD), which uses radar sensors or cameras to detect vehicles in the blind spot area and alerts the driver to avoid a collision when changing lanes. However, this kind of BSD system fails to notify nearby vehicle drivers in this blind spot of the possible collision. The goal of this research is to design a proactive bus blind spot warning (PBSW) system that will immediately notify motorcyclists when they enter the blind spot or the area of the inner wheel difference of a target vehicle, i.e., a bus. This will increase the real-time functionality of BSD and can have a significant impact on enhancing motorcyclist safety. The proposed hardware is placed on the motorcycle and consists of a Raspberry Pi 3B+ and a dual-lens stereo camera. We use dual-lens cameras to capture and create stereoscopic images then transmit the images from the Raspberry Pi 3B+ to an Android phone via Wi-Fi and to a cloud server using a cellular network. At the cloud server, we use the YOLOv4 image recognition model to identify the position of the rear-view mirror of the bus and use the lens imaging principle to estimate the distance between the bus and the motorcyclist. Finally, the cloud server returns the estimated distance to the PBSW app on the Android phone. According to the received distance value, the app will display the visible area/blind spot, the area of the inner wheel difference of the bus, the position of the motorcyclist, and the estimated distance between the motorcycle and the bus. Hence, as soon as the motorcyclist enters the blind spot of the bus or the area of the inner wheel difference, the app will alert the motorcyclist immediately to enhance their real-time safety. We have evaluated this PBSW system implemented in real life. The results show that the average position accuracy of the rear-view mirror is 92.82%, the error rate of the estimated distance between the rear-view mirror and the dual-lens camera is lower than 0.2%, and the average round trip delay between the Android phone and the cloud server is about 0.5 s. To the best of our knowledge, this proposed system is one of few PBSW systems which can be applied in the real world to protect motorcyclists from the danger of entering the blind spot and the area of the inner wheel difference of the target vehicle in real time.