In recent years, autonomous driving technology has progressed rapidly, promising to transform current transportation systems significantly and attracting growing public attention. Previous studies have predominantly relied on interviews and surveys to examine public perceptions of autonomous vehicles (AVs), which come with inherent limitations. This study utilizes a substantial dataset of 616,355 samples from Sina Weibo, one of China’s most prominent social media platforms, to examine variations in statistical, temporal, spatial, and emotional dimensions to gain insights into the Chinese populace’s perceptions and sentiments towards AVs. From 2015 to 2023, public attention and engagement have steadily risen, with individual users representing the largest group interested in AVs. Among individual users, males and young adults (aged 18–30) have demonstrated heightened interest. Attention levels are particularly pronounced in economically developed regions such as Beijing, Guangdong, and Shanghai. Overall, the public attitude towards AVs is positive; however, there has been a significant rise in negative sentiment since 2020, primarily related to concerns about safety and technological reliability. Based on public attention, this study also discusses potential challenges and corresponding strategies. These insights gained will aid automobile manufacturers, technology firms, and public agencies in addressing emerging challenges and facilitating the development of AVs.

Compared with administrative cities, natural cities can be generally referred to as the areas generated based on the density of different urban facilities (e.g., point of interest, road network, etc.). To some extent, natural cities are outperformed in some related urban studies, such as urban living structure analysis. Nevertheless, traditional ways of generating natural cities are mostly limited to the planar space. Modern cities such as Hong Kong are vertical cities with high buildings, 3D road networks and land uses. Therefore, traditional nature cities could be biased when applied to 3D cities. In this work, a 3D road network in Hong Kong is adopted to extract true road intersections and generate modified natural cities to explore urban living structures. Numerous living structure units are classified into two parts: tiny and serried ones representing natural cities and vast ones representing rural areas. The classification method applies head/tail breaks, and a clustering algorithm was fitted for heavy-tailed distribution. According to the living structure theory, the living structures of the proposed natural cities and traditional natural cities based on the same road network in Hong Kong are compared. The findings show that the distribution of modified natural city regions is more reasonable compared with typical ones. The improved model will more clearly show the inherent living structure of the city and will allow an analysis of the relationship between the part and wholeness of the city.