Driverless, SAE Level 4 automated vehicles (AVs)—vehicles operating without on-board human operators—have become operational in some cities in the U.S. The driving style and behaviors of AVs can induce changes in the behavior of road users interacting with AVs in traffic. Prior research has not collected data from road users residing in areas in which AVs are deployed and who have solid experience with AVs by regular interactions with them. As a result, a comprehensive and rich analysis of road users’ responses to AVs in traffic based on solid experience and the underlying reasons is missing. The two main research questions of this study are: 1) How do road users respond to AVs in traffic? and 2) Which factors affect road users’ responses to AVs in traffic? Semi-structured interviews were conducted with individuals residing in U.S. cities in which driverless AVs are deployed to explore how and why road users respond to driverless AVs in traffic. Content analysis was applied to manually identify themes in the data, complemented by using large language models. We also computed Spearman rank-order correlations to determine significant associations between the sub-themes. The most common road user behaviors were being more cautious around AVs, letting the AV pass and waving and gawking at them. Road users took advantage of the capabilities of AVs, cutting them off, slowing them down, or recklessly crossing the road in front. The AV safety operators typically monitored the operation of the AV, contributing to the perception that AVs are safe and predictable. Other participants reported incidences of inattentive drivers / human operators of Tesla’s SAE Level 2 partially automated driving system, being observed sleeping in the AV and rear-ending one of our participants. The most common external communication cue between road users and human drivers was eye contact, in some cases also when there was no operator present. Media reports / personal stories involving fatal accidents with AVs, particularly those linked to Tesla’s partially automated driving system, were linked to concerns about AV safety. Our study reveals significant associations between the behavior of AVs (e.g., AV being stuck) and road users’ changes in behavior, cognition (e.g., trust, distrust) and affect (e.g., perceived safety, frustration or anger). More trials with AVs on public roads can promote the interest and curiosity of road users, and their acceptance and use of AVs. The need for eHMIs and their effectiveness in promoting safer, more efficient, and comfortable interactions needs to be further investigated.

This study applies an extended version of one of the most popular technology acceptance models, the Unified Theory of Acceptance and Use of Technology (UTAUT2), to predict user acceptance of SAE Level 3 conditional automated driving among more than 9,000 car drivers from nine European and non-European countries. We extend the model by two factors, trust and teaming, that we consider pivotal for user acceptance of conditional automated driving. We also investigate the factors impacting the determinants of acceptance and use of conditional automated driving, addressing a well-known gap in research. In this study we find that 40% of respondents did not intend to buy, and 39% of respondents did not express an intention to use conditional automated driving when available. 71% of respondents indicated a preference to stay engaged in the driving task to respond to requests from the car to resume manual control. The structural equation modeling analysis revealed that performance expectancy is the strongest predictor of driver’s behavioral intentions to use conditional automated driving, followed by trust and social influence. Contrary to common beliefs positioning trust as one of the most influential drivers of user acceptance of AVs, the influence of trust on behavioral intention to use conditional automated driving is small. The availability of facilitating conditions supporting the use conditional automated driving (e.g., knowledge, getting help from friends, family, or car dealers) has a small influence on the acceptance of AVs. We also found significant effects of the factors impacting the determinants of acceptance and use. The effect of performance expectancy on hedonic motivation is positive, suggesting that the perceived usefulness positively enhances the perceived enjoyment. Similarily, the effect of social influence on performance expectancy and trust is positive, suggesting the social network of the individual plays an important role in promoting positive beliefs about the effectiveness of the technology and trust in the technology. Access to participation in the questionnaire was limited to respondents with access to internet, which is why future research should be performed with respondents without internet accessibility to examine differences in attitudes and conditional automated driving acceptance between these internet-affine and less internet-affine groups.

Automated driving can fundamentally change road transportation and improve quality of life. However, at present, the role of humans in automated vehicles (AVs) is not clearly established. Interviews were conducted in April and May 2015 with 12 expert researchers in the field of human factors (HFs) of automated driving to identify commonalities and distinctive perspectives regarding HF challenges in the development of AVs. The experts indicated that an AV up to SAE Level 4 should inform its driver about the AV's capabilities and operational status, and ensure safety while changing between automated and manual modes. HF research should particularly address interactions between AVs, human drivers and vulnerable road users. Additionally, driver-training programmes may have to be modified to ensure that humans are capable of using AVs. Finally, a reflection on the interviews is provided, showing discordance between the interviewees’ statements – which appear to be in line with a long history of HFs research – and the rapid development of automation technology. We expect our perspective to be instrumental for stakeholders involved in AV development and instructive to other parties.