Smart and Connected Scooter for People with Mobility Challenges

Abstract

Despite the technical success of existing assistive technologies, for example, electric wheelchairs and scooters, they are still far from effective enough in helping the people with mobility challenges, for example, the elderly, blind, and disabled, navigate to their destinations in a hassle-free manner. While mobility scooters may help to improve the quality of life of their users, operating them is still challenging in many scenarios. Riders often face challenges in driving scooters in some indoor and crowded places, especially on sidewalks with numerous obstacles and other pedestrians. It is also hard to determine the right path, for example, sidewalks with ramps and no dead ends. People with certain disabilities, such as the blind, are often unable to drive their scooters well enough. In this paper, we propose to improve the safety and autonomy of navigation by designing a cutting-edge autonomous scooter, thus allowing people with mobility challenges to ambulate independently and safely in possibly unfamiliar surroundings. To solve the discrepancies of system complexity, sensing, and mapping we propose sensor fusion solutions and connected infrastructure for object localization and mapping under various spatial and lighting conditions. We propose to improve obstacle detection system by performing joint partitioning and deep learning based object detection. Our system design offers the advantages of being affordable, vendor-independent, discreet, noninvasive and unobtrusive.