One of the most important systems for smart cities is an edge computing system to facilitate decision-making by using real-time awareness and data analytics. Although existing works focused on the safety of pedestrians, the safety of personal-mobility vehicles is what smart cities need to address toward the future. On-board sensors could be easily disabled because of external issues of hardware caused by dirt or snow, internal issues of hardware, and software issues such as malware infection. We propose an edge computing system that supports robust vehicle automation with a sensor network using multiple light-detection-and-ranging (LIDAR) sensors deployed like roadside cameras or indoor security cameras for surveillance for personal-mobility vehicles. We develop a prototype system of the proposed system using real LIDAR units and a mobility scooter and evaluate the system in terms of route errors to verify the feasibility of the proposed system with a scenario where the vehicle is enabled to continue self-driving even when its on-board sensor is disabled.