# Enhancing Driving Safety of Personal Mobility Vehicles Using On-Board Technologies
## 3 System Architecture, Design, and Implementation
### Enhancing Driving Safety by Ultrasonic Sensors and Emergency Stop  
Personal mobility devices have low posture stability, and the rider is exposed to the outside, so there is a risk of overturning or falling accidents. As shown in Figure 8, the device developed in this research can be manufactured in a form that can be attached to a personal mobility device. In addition, this device analyzes the information acquired through the front camera and front and rear sensors in the on-device controller and provides text-to-speech service to the driver. Therefore, by utilizing this device, the driver of the personal mobility device can receive additional voice guidance while keeping an eye on the situation ahead. The following sensor and emergency stop technology were applied to help passengers of personal mobility devices recognize dangerous situations and to notify them of obstacle detection to prevent accidents. As a specific implementation method, some sensors (such as ultrasonic wave) modules are connected to a microprocessor and are used to generate an emergency stop of the personal mobility device when an obstacle is detected. The sensors recognize obstacles in front through ultrasonic sensors and process the signal from the sensors. The controller board notifies the forward collision warning if the vehicle is within a predetermined range. It can stop by itself when approaching an obstacle by recognizing objects and can ask for help from people around it in various ways through smartphone applications. As shown in Figure 9, we designed and implemented a system to enhance the safety of personal mobility devices, such as electric wheelchairs, by integrating a camera with various sensors. The main components of the proposed system include an ultrasonic sensor, a front camera, an embedded platform (NVIDIA Orin board), Arduino, and multiple sensor modules. The system detects objects, predicts potential hazards using sensor data and front camera images, and alerts users with visual impairment. An emergency stop is a safety mechanism that shuts down machinery in an emergency when other methods are inadequate. It is also known as a kill switch, emergency brake, emergency off, or emergency power off. This is a kind of emergency technique that can allow an attendant to stop a powerchair via a controller depending on the situation. It is great for carers and parents of young children and the elderly, or in situations where a carer needs to step in to prevent an accident or injury. This provides carers and attendants greater peace of mind when out and about with a wheelchair user. When an emergency stop is generated, the emergency stop control device latches in and must be manually released. The emergency stop function should take precedence over all other functions and should shut off the energy supply to hazardous drives as quickly as possible. Figure 8: Schematics for sensing forward obstacles, and for automatic brake system. Figure 9: Hardware components and control interface of the motorized wheelchair system. The server system architecture facilitates universal execution. As shown in Figure 10, the smartphone application was built using Progressive Web App (PWA) technology on a React and Vite framework and provides a native app-like experience with offline functionality. The on-device object recognition device (see Figure 11) was mounted on an electric wheelchair to detect forward obstacles and provide voice guidance. This setup enhances the safe mobility of visually impaired users and improves the quality of life of various vulnerable social groups. To perform object detection on embedded devices, we perform learning on an existing server rather than learning the model on the embedded board, and then we deploy it to the edge of hardware with limited computing resources. In conventional embedded boards, it is advantageous to use 8-bit integer computation for the weights reflected in the neural network.