An AI Safety Monitoring System for Electric Scooters Based on the Number of Riders and Road Types

• Published in: Sensors (Basel, Switzerland) Vol. 23; no. 22; p. 9181
• Main Authors: Jang, Woo-Jin, Kim, Dong-Hyun, Lim, Si-Hyung
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.11.2023
• Subjects: accelerometer; Accelerometers; Accidents; Artificial intelligence; Cameras; conductive film; Data collection; Drunk driving; electric scooter; Electric vehicles; Helmets; Neural networks; Pedestrians; Roads & highways; Safety equipment; Scooters; Sensors; Surveillance; Traffic accidents & safety; Transportation planning; Walkways; South Korea
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s23229181

Electric scooters are quickly becoming a popular form of mobility in many cities around the world, which has led to a surge in safety incidents. Moreover, electric scooters are not equipped with safety devices for riders, which can lead to serious accidents. In this study, a footrest, data-collection module, and accelerometer module for electric scooters were developed to prevent various accidents caused by the rapid increase in the use of electric scooters. In the experiment, the boarding data of the electric-scooter riders were collected from the footrest and data-collection module. Moreover, the driving data of the electric scooters for different road types were collected with the accelerometer module. We then trained two artificial intelligence (AI) models based on convolutional neural networks (CNNs) for different types of data. When we considered the learning accuracy and mean square error (MSE), which are performance indicators of the ability of the trained AI models to discriminate data, for each AI model, the learning accuracy converged to 100% and the MSE converged to 0. Further, this study is expected to help reduce the accident rate of electric scooters by resolving the causes of frequent accidents involving electric scooters around the world.