# Enhancing Driving Safety of Personal Mobility Vehicles Using On-Board Technologies
## 1 Introduction  
The demand for electric wheelchairs has steadily increased in response to the aging population. According to a report from the Korea Consumer Agency (Figure 1), 35.5% of users of motorized assistive devices (including electric wheelchairs and scooters) have experienced accidents [1, 2]. These accidents stem from a variety of causes, including catching on curbs or obstacles (42.2%), collisions with external obstacles (36.3%), unexpected halting during operation (32.4%), collisions with vehicles (24.5%), collisions with pedestrians (22.5%), loss of balance due to wheelchair tilt (12.7%), entrapment in device components (10.8%), and mechanical malfunctions or fires (8.8%). Such accidents can lead to severe injury and inconvenience to users. In fact, the most recent statistics on assistive device accidents from researchers indicate the comparatively low level of attention paid to the issue compared to other safety equipment [2, 3]. Personal mobility devices can have various sensors and components, including ultrasmall front/rear/side cameras, impact sensors, communication modules, and microcontrollers. When applied, this system enables an electric kickboard to detect its surroundings autonomously, identify abnormal signs, adjust driving speed, or report damage or accidents to a control center in real time. To improve personal mobility safety, this study incorporates a vision recognition function that detects and recognizes the surroundings using an ultra-small camera. This feature can be enhanced to recognize pedestrians ahead, execute an emergency stop, or detect and reduce speed when operating on sidewalks rather than dedicated roads. In addition, integrating a GPS receiver can enable automatic speed adjustments when entering child and elderly protection zones through more precise location tracking. Furthermore, combining the vision recognition function with impact sensors allows comprehensive road condition assessment and automatic speed adjustments when necessary. As shown in Figure 2 below, the steady increase in the use of personal mobility devices has led to a rise in accident rates. Users of electric wheelchairs are more prone to accidents due to low postural stability and greater exposure to external impacts, which increases their physical and psychological burdens. However, institutional measures regarding support policies for electric mobility aids, accident prevention strategies, and protective equipment remain insufficient. Figure 1: Types of accidents experienced by motorized assistive device users [1]. Figure 2: Supply trends of electric and manual wheelchairs (2017–2023). To prevent these accidents and ensure the safety of electric wheelchair users, obstacle recognition-based warning and sensing-based emergency stop play key roles in this paper. So, our research aims to develop a system to enhance the safety of electric wheelchairs using those two key technologies: obstacle recognition-based warning and sensing-based emergency stop. In particular, the reason why this study can achieve fast performance without relying on high-performance computation support and API calls through existing large servers is as follows. First, this study is implemented by utilizing an embedded board with on-device AI technology that implements AI applications with a GPU. In addition, this study utilizes a hardware accelerator for deep-learning inference to perform computationally intensive deep-learning tasks, such as convolution, much more efficiently than CPUs. The contribution of this paper to safety-enhancing techniques of personal mobile vehicles (PMVs) is that the driving safety enhancement device we have developed performs object detection on the embedded board itself using hardware acceleration. It is also equipped with control logic for emergency stops. With the recent trend of increasing accidents involving personal mobile device users, we believe that the results of this research and development will be of interest and competitiveness.