Visual impairment or even loss seriously affects quality of life. Benefited by the rapid development of sound/laser detection, Global Positioning System (GPS)/Beidou positioning, machine vision and other technologies, the quality of life of blind people is expected to be improved through visual substitution technology. The existing visual substitution devices still have limitations in terms of safety, robustness, and ease of operation. The remote companion system developed here fully utilizes multimodal navigation and remote communication technologies, and the positioning and interaction functions of commercial mobile phones. Together with the accumulated judgment of backend personnel, it can provide real-time, safe, and reliable navigation services for blind people, helping them complete daily activities such as independent travel, circulation, and shopping. The practical results show that the system not only has strong operability and is easy to use, but also can provide users with a strong sense of security and companionship, making it suitable for promotion. In the future, this system can also be promoted for other vulnerable groups such as the elderly.

Remote-sighted assistance (RSA) is a technology designed to provide assistance for visually impaired people (VIPs). In this scene, a remote-sighted agent communicates and sends commands to navigate and assist VIPs via real-time video sent back. However, the latency in real-time video and the deviation in the execution of instructions by VIPs are two important factors that affect the performance of agents to guide them. Therefore, how to enable agents to better guide VIPs under conditions of video transmission latency and deviation in instruction execution is an important issue. In this paper, we utilize Unreal Engine to create a virtual training platform for RSA, which simulates VIPs executing instructions in the real world and resembles the environment in RSA systems. We aim to help remote-sighted agents quickly master the set of vibration commands formed after encoding tactile vibrations and enable them to guide VIPs more effectively. Our experiment results show that, compared with untrained novices, when guiding people through the same path, agents trained on this platform reduce their average time by 32.09% and their average number of contacts with the environment by 57.57%. Our work provides agents with a simple and convenient simulation and training platform designed to enhance their performance by guiding VIPs with less travel time and fewer environmental contacts. Through this platform, agents can more effectively assist the visually impaired.

Visually impaired people face urban mobility hazards such as vehicles, pedestrians, curbs, and steps. Wearable electronic travel aids can support obstacle avoidance only when these hazards fall within sensing coverage. Existing studies have focused mainly on algorithms and human–computer interaction, while the sensing coverage required by real-world hazards remains less examined. We collected 1486 min of street-scene video, built urban mobility hazard samples, and used simulation to compare target pixel counts over time for cameras with different horizontal fields of view. Among 81,353 valid samples, hazards showed concentrated high-frequency categories and a rich long tail, mainly including vehicles, scooters, pedestrians, curbs, bollards, poles, trees, and steps. Simulation showed that a horizontal field of view of approximately $120°$ balanced pixel coverage and sustained observation. We then built a chest-mounted four-camera prototype and conducted a feasibility demonstration at a real intersection. Camera configuration should therefore be guided by real-world hazard visibility, and coordinated multi-camera coverage is a promising design choice for wearable ETAs in urban environments.