Citation

Jing P, Cai Y, Wang B, Wang B, Huang J, Jiang C, Yang C. Transp. Res. F Traffic Psychol. Behav. 2023; 93: 248-265.

Copyright

(Copyright © 2023, Elsevier Publishing)

DOI

10.1016/j.trf.2023.01.018

PMID

unavailable

Abstract

In China, vehicles with L2 driving automation are already widespread, while L3-L5 automated vehicles (AVs) are not allowed on the road. Surprisingly, crashes involving vehicles with L2 driving automation contributed to the public's discussion of L3-L5 AVs. Understanding the variations in public perception of AVs is imperative, which could significantly affect the future evolution of AVs. Respondents have few opportunities to express their views freely in survey-based studies relying on structured questionnaires (e.g., Likert scale). We collected 42,111 comments from Chinese mainstream social media platforms (Sina Weibo and Tik Tok) in August 2021 and applied advanced text mining technology (Latent Dirichlet Allocation topic model, text network analysis, and sentiment analysis) to understand variations in public perception of AVs before and after the crashes. The results show that the public expresses more blame for vehicles with L2 driving automation than human drivers and has misunderstandings about the current development of L3-L5 AVs, which is closely connected with publicity from automobile companies and media. Women were more negative than men both before and after the crash, and the sentiment difference was most significant in the first three days of the crash. This research provides new ideas for a timely understanding of the public perception of AVs.

FINDINGS contribute to specific policy recommendations to help in the technological change and wider adoption of AVs.

Language: en

Keywords

Automated vehicles; Latent Dirichlet Allocation topic model; Public perception; Sentiment analysis; Social media comments; Text network analysis