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.

If automated vehicles (AVs) are to move efficiently through the traffic environment, there is a need for them to interact and communicate with other road users in a comprehensible and predictable manner. For this reason, an understanding of the interaction requirements of other road users is needed. The current study investigated these requirements through an analysis of 22 h of video footage of the CityMobil2 AV demonstrations in La Rochelle (France) and Trikala (Greece). Manual and automated video-analysis techniques were used to identify typical interaction patterns between AVs and other road users. Results indicate that road infrastructure and road user factors had a major impact on the type of interactions that arose between AVs and other road users. Road infrastructure features such as road width, and the presence or absence of zebra crossings had an impact on road users’ trajectory decisions while approaching an AV. Where possible, pedestrians and cyclists appeared to leave as much space as possible between their trajectories and that of the AV. However, in situations where the infrastructure did not allow for the separation of traffic, risky behaviours were more likely to emerge, with cyclists, in particular, travelling closely alongside the AVs on narrow parts of the road, rather than waiting for the AV to pass. In addition, the types of interaction varied considerably across socio-demographic groups, with females and older users more likely to show cautionary behaviour around the AVs than males, or younger road users. Overall, the results highlight the importance of implementing the correct infrastructure to support the safe introduction of AVs, while also ensuring that the behaviour of the AV matches other road users’ expectations as closely as possible in order to avoid traffic conflicts.