Numerical investigation of the rider's head injury in typical single-electric self-balancing scooter accident scenarios. Issue 194 (21st September 2022)

• Record Type: Journal Article
• Authors: Wang, Fang; Huang, Jiaxian; Hu, Lin; Hu, Shenghui; Wang, Mingliang; Yin, Jiajie; Zou, Tiefang; Li, Qiqi
• Is Part Of: Journal of the Royal Society interface. Volume 19:Issue 194(2022)
• Publication Date: 2022-09-21
• Subjects: electric self-balancing scooter (ESS) -- single-ESS accident scenario -- head/brain injury -- computational biomechanics model; Physical sciences -- Research -- Periodicals; Life sciences -- Research -- Periodicals; Interdisciplinary research -- Periodicals; 570.5
• DOI: 10.1098/rsif.2022.0495
• Languages: English
• ISSNs: 1742-5689

Abstract

Abstract : As the use of electric self-balancing scooters (ESSs) increases, so does the number of related traffic accidents. Because of the special control method, mechanical structure and driving posture, ESSs are prone to various single-vehicle accidents, such as collisions with fixed obstacles and falls due to mechanical failures. In various ESS accident scenarios, the rider's head injury is the most frequent injury type. In this study, several typical single-ESS accident scenarios are reconstructed via computational methods, and the risk of riders' head/brain injury is assessed in depth using various injury criteria. Results showed that two types of ESSs (solo- and two-wheeler) do not have clear differences in head kinematics and head injury risks; the head kinematics (or falling posture) and ESS accident scenario exhibit a distinct effect on the head injury responses; half of the analysed ESS riders have a 50% probability of skull fracture, a few riders have a 50% risk of abbreviated injury scale (AIS) 4+ brain injury, and none has a diffuse axonal injury; the ESS speed plays an important role in producing the head/brain injury in ESS riders, and generally, higher ESS speed generates higher level of predicted head injury parameters. These findings will provide theoretical support for preventing head injury among ESS riders and data support for developing and legislating ESSs.