机电工程技术2026，Vol.55Issue(6)：148-155,8.DOI:10.3969/j.issn.1009-9492.2026.06.024
基于STM32的电动自行车智能安全系统设计
Design of Intelligent Safety System for Electric Bicycle Based on STM32
摘要
Abstract
As the usage rate of electric bicycles continues to rise,both the incidence of traffic accidents and violations are also trending upwards.In response to this situation,the STM32 microcontroller is used as the core control unit,integrating visual module,battery safety monitoring module,driver behavior monitoring module,driving status safety monitoring module,and alarm system,and a mobile App is built to achieve bidirectional data interaction.The system employs the YOLOv10 algorithm,which can accurately recognize the driver´s helmet wearing condition and mental state within 200 ms,achieving a detection accuracy rate of over 96%;the PID algorithm can automatically adjust the vehicle speed based on road conditions and driving status,ensuring a speed control error of less than±0.5 km/h;a filtering algorithm is used to optimize sensor data accuracy,reducing data fluctuation errors by 60%.Verified by actual testing,the battery temperature control strategy can maintain the battery operating temperature fluctuation within±2℃;the PID speed control algorithm improves energy recovery efficiency by over 30%;the YOLOv10 algorithm significantly shortens the response time for helmet and facial detection,enhancing detection efficiency by about 40%.This intelligent safety system realizes real-time monitoring of the driver´s status,blind spot detection alarms,automatic speed adjustment,and anti-collision functions,which can promptly correct inappropriate driver behavior.Through simulated road testing,the simulated incidence rate of electric bicycle accidents is reduced by 58%,providing reliable technical support for safe travel on electric bicycles.关键词
YOLOv10 算法/PID控制算法/单片机/头盔检测/温度控制Key words
YOLOv10 algorithm/PID control algorithm/microcontroller/helmet detection/temperature control分类
信息技术与安全科学引用本文复制引用
侯晓丽,黄丽霞,陈锦阳,杨晓彤,张再鹏,严继超..基于STM32的电动自行车智能安全系统设计[J].机电工程技术,2026,55(6):148-155,8.基金项目
2023年广东省大学生创新训练项目(S202313656021) （S202313656021）
