Selecting Beijing's Baitasi hutong as a case study, this paper explores a new approach that combines social sciences and computer sciences methodologies to answer a research question around sustainable urban regeneration for the historic area within dense inner-city. The study consists of two parts: 1) Data collection and analysis include: conducting a field audit at the study area collecting street view image data and multisource information on mobility, living conditions, and social behavior, then running computer-assisted geospatial analysis with data retrieved from image recognition through Baidu APIs and OpenCV using Python language; 2) Proposing an integrated vision of “Streets for People, Hutongs for All” toward a human-centered inclusive urban future in the era of self-driving vehicles, as well as policy recommendations on collaborative creation and governance to ensure the implementation of new design plan.