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.

Ensuring safety remains one of the biggest challenges for the widespread adoption of automated vehicles (AVs). Remote operation of AVs is a promising approach to address this, allowing remote operators to intervene when AVs encounter edge cases. However, remote operators are out-of-the-loop from the conventional driver in vehicle environment interaction, impacting their situation awareness and ability to safely control or assist the vehicle. In the scenario of remote driving, this is more evident since multimodal feedback is required to replicate the conventional driver-vehicle environment-interaction. In addition to visual and auditory modalities, motion feedback has been proposed as a way to bridge the gap between remote driving and in-vehicle driving. However, since motion feedback is cost-intensive, it might hinder rapid upscaling of remote driving systems. Thus, this study evaluated whether motion feedback adds value to driving performance and experience of the remote operator in low-velocity scenarios. Driving performance and experience were assessed and compared using objective and subjective metrics in three conditions (in-vehicle driving, and remote driving with and without motion feedback). The findings show that in remote driving, motion feedback fails to provide significant improvements. When compared to in-vehicle driving, remote driving performance and experience remain significantly worse. This suggests that motion feedback, in its current form, is redundant in low-velocity scenarios and that a simplified Remote Driving Station (RDS) may be sufficient in these scenarios. Future work should optimize simplified RDS designs, enhance feedback and human-machine interfaces and explore different driving scenarios for safe and efficient remote driving.

The investigation of automated vehicle acceptance (AVA) has received considerable attention in the past few years. Understanding the factors impacting their acceptance is pivotal to ensure a large-scale and wide acceptance of AVs. The AVA by pedestrians is still little understood. To address this knowledge gap, the main objective of this study is to develop and validate an instrument for the assessment of AVA by pedestrians. We tested this instrument on a German sample of pedestrians (n = 136), considering their individual demographic characteristics, and level of affinity for technology interaction. A four-step approach was adopted to analyze the data. First, a principal component analysis was performed to reduce the number of items, exploring the sources of variation in the dataset. Second, the principal components were subjected to a confirmatory factor analysis to investigate the validity and reliability of the proposed measurement model. Third, structural equation modeling was conducted to estimate the path relationships between our constructs. The study has revealed differences between the effect sizes and significance levels of the factors influencing pedestrians’ AVA. The AVA by pedestrians was most strongly influenced by affinity for technology interaction (i.e., extent to which the individual actively approaches or avoids the interaction with new systems), performance expectancy (i.e., extent to which the individual believes that using the system will support them in achieving gains in the performance of the task) and social influence (i.e., extent to which the individual believes that people important to them think that the individual should perform the behavior). Male pedestrians were more likely to accept AVs. We also revealed significant interaction effects of age on the variables in our model. With this work, we have contributed to the development and validation of the Automated Vehicle Acceptance Questionnaire for Pedestrians (AVAQ-P). We recommend future research to replicate the study with a larger, more representative and gender-diverse population of pedestrians, considering cross-cultural differences in AVA.