Citation

Joo YK, Jeong MW, Kim B. Transp. Res. F Traffic Psychol. Behav. 2023; 93: 266-279.

Copyright

(Copyright © 2023, Elsevier Publishing)

DOI

10.1016/j.trf.2023.01.012

PMID

unavailable

Abstract

In spite of a growing number of studies regarding collision algorithms of autonomous vehicles (AVs), little attention has been paid to how AV types influence the effects of collision algorithms on individuals' attitudes toward AVs. The current study investigated whether police AVs with selfish or utilitarian collision algorithms, compared with personal AVs with the identical type of collision algorithms, induce different attitudes toward AVs. An online experiment was conducted to explore how the AV types (personal vs police) and collision algorithms (selfish vs utilitarian) can jointly influence individuals' attitudes, such as trust, perceived benefit, ethical evaluation of AVs, and public acceptance of AVs. According to our findings, there was a moderating effect of AV types on how individuals respond to AVs with selfish or utilitarian collision algorithms. Police AVs with selfish algorithms had a negative impact on individuals' overall attitudes toward AVs. In contrast, personal AVs with selfish collision algorithms did not lead to such negative attitudes. Furthermore, selfish algorithms embedded in personal AVs were not found to increase individuals' perceived benefit or approval for the mass adoption of AVs. The findings suggest that deciding collision algorithms for AVs should involve a careful consideration of the AVs' roles and purposes.

Language: en

Keywords

Automated vehicles; AV type; Collision algorithms; Police AVs; Selfish algorithms; Utilitarian algorithms