Abstract
Automated vehicles (AVs) offer human operators the opportunity to participate in non-driving activities while on the move. In this study, we examined and compared drivers' perception of non-driving activities in two driving modes: highly AVs in the future and current vehicle systems, where the human operator is still responsible for controlling the vehicle such as braking and steering. The study used a survey distributed through an online paid marketplace platform called Lucid, which included open-ended questions soliciting participants' perceptions of non-driving activities given a work commute scenario for each driving mode. Text mining and clustering analysis were used to analyze the responses of 752 participants to four open-ended survey questions. Results showed that drivers had a more positive sentiment towards future automated vehicles compared to current systems. The most reported non-driving activities overall were “work”, “listen”, and “relax”; were “listen” for current vehicle systems and “work” for AVs. The study also captured the changes in drivers' perception from current systems to AV systems. The findings indicated that most drivers (83.4%) would continue their current non-driving activities, with 76.0% continuing to perform work or work-related activities. Approximately 8.7% of respondents would switch from their current tasks to work-related tasks in an AV, while 3.7% would do the opposite—abandon work-related tasks to do other activities. The study suggests that working while commuting will be an advantage of AVs, highlighting the need to understand how people can work productively as we move forward with automated vehicles.
Original language | English (US) |
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Pages (from-to) | 305-320 |
Number of pages | 16 |
Journal | Transportation Research Part F: Traffic Psychology and Behaviour |
Volume | 94 |
DOIs | |
State | Published - Apr 2023 |
Keywords
- Automated vehicles
- Commuting
- Non-driving tasks
- Text mining
ASJC Scopus subject areas
- Civil and Structural Engineering
- Automotive Engineering
- Transportation
- Applied Psychology