Investigation of driver preference for a user-centred design of decision systems in autonomous vehicles, part I: preferences for binary self-driving modes

Abstract

As autonomous vehicles (AV) are becoming more pervasive in transportation, it is important to consider drivers’ perceptions of these vehicles. The existing research has investigated taking over AV control, its safety and acceptance. However, the preferences for self-driving in multiple traffic situations have not been extensively investigated. In Part I, we aim to bridge these gaps by investigating such preferences in high and low traffic complexities. Eighty-eight participants in North America were recruited. They viewed video recordings of driving in the city of Toronto, the regional municipality of Waterloo and highways to answer survey questions. Their responses regarding perceptions and preferences were simply analysed using descriptive statistics and Chi-square test at various traffic situations with two traffic complexities. It showed strong preferences for self-driving in most low complexity situations and certain situations in both complexities. These findings can suggest a few applicable design principles of AV decision system regarding traffic situation-based and biased perceptions-based user preferences. In Part II, we extend our analyses to user preferences for multiple two-stage actions of AVs and suggest additional design principles of the system with a more-in-depth insights.

Keywords:

• Individual perception
• driver preference
• autonomous vehicles
• traffic complexity
• traffic situation
• user-centred design

Acknowledgments

We would like to thank So Yeon Park, David Sirkin, Dylan James Moore, and Rebecca Currano at Stanford University for sharing video clips in their paper (Park, Moore, and Sirkin 2020) and for useful feedback to start our further research on driver preference.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research was funded by a Discovery grant from the Natural Sciences and Engineering Research Council (RGPIN 2019-05304) to S.S.