Technological advances are bringing connected and autonomous vehicles (CAVs) to the ever- evolving transportation system. Anticipating the public acceptance and adoption of these technologies is important. A recent internet-based survey was conducted polling 347 Austinites to understand their opinions on smart-car technologies and strategies. Ordered-probit and other model results indicate that respondents perceive fewer crashes to be the primary benefit of autonomous vehicles (AVs), with equipment failure being their top concern. Their average willingness to pay (WTP) for adding full (Level 4) automation ($7,253) appears to be much higher than that for adding partial (Level 3) automation ($3,300) to their current vehicles. This study estimates the impact of demographics, built-environment variables, and travel characteristics on Austinites’ WTP for adding such automations and connectivity to their current and coming vehicles. It also estimates adoption rates of shared autonomous vehicles (SAVs) under different pricing scenarios ($1, $2, and $3 per mile), choice dependence on friends’ and neighbors’ adoption rates, and home-location decisions after AVs and SAVs become a common mode of transport. Higher-income, technology-savvy males, living in urban areas, and those who have experienced more crashes have a greater interest in and higher WTP for the new technologies, with less dependence on others’ adoption rates. Such behavioral models are useful to simulate long-term adoption of CAV technologies under different vehicle pricing and demographic scenarios. These results can be used to develop smarter transportation systems for more efficient and sustainable travel.

Assessing Public Opinions of and Interest in New Vehicle Technologies: An Austin Perspective

The purpose of this study was to explore factors affecting users’ acceptance of automated vehicles (AVs, Level 3). A theoretical acceptance model was proposed by extending the Technology Acceptance Model (TAM) with new constructs: initial trust and two types of perceived risk (i.e., perceived safety risk [PSR] and perceived privacy risk [PPR]). It was hypothesized that initial trust was built upon perception factors (i.e., perceived usefulness [PU], perceived ease of use [PEOU], PSR, and PPR) and was a key determinant of AV acceptance. The validity of the model was confirmed with a structure equation modeling analysis based on data collected from 216 survey samples. Results revealed that initial trust was the most critical factor in promoting a positive attitude towards AVs, which, together with PU, determined users’ intention to use AVs. Initial trust could be enhanced by improving PU and reducing PSR associated with AVs. Theoretically, these findings suggest that initial trust offers another and probably more important pathway for other factors to impact consumers’ adoption of systems with uncertainty. Practically, the findings provide guidance for designing interventions aimed at improving public’s acceptance towards AVs.

The roles of initial trust and perceived risk in public’s acceptance of automated vehicles

This field study aims at understanding the influence of direct experience of an automated vehicle (AV, Level 3) and explaining and predicting public acceptance of AVs through a psychological model. The model includes behavioral intention (BI) to use self-driving vehicles (SDVs, Level 5), willingness to re-ride (WTR) in our AV (Level 3), and their four potential determinants, namely perceived usefulness (PU), perceived ease of use (PEU), trust related to SDVs, and perceived safety (PS) while riding in our AV. The last two determinants are largely ignored, but we consider them critical in the context of AVs. Three-hundred students were invited as participants (passengers) to experience the AV. The trust, PU, PEU, and BI of the participants were recorded prior to their experiencing the AV; after this experience, all the constructs of the psychological model were recorded. The participants’ experience with the AV was found to increase their trust, PU and PEU (but not BI), the consistency between PU/PEU and BI, and the explanatory power of BI. The model explained 55% of the variance in BI and 40% in WTR. PU, trust, and PS were found to be steady and direct predictors of both the acceptance measures; PEU predicted BI only after the participants’ AV experience. Mediation analysis showed that trust also can indirectly affect AV acceptance through other determinants. Out-of-sample prediction confirmed the model’s predictive capability for AV acceptance. The theoretical contributions and practical implications of the findings are discussed.

What drives people to accept automated vehicles? Findings from a field experiment

This paper provides a review of studies published in peer-reviewed journals, conference proceedings, and technical academic and private sector reports on surveys about autonomous vehicles (AVs) from 2012 onward. The studies and respective surveys are categorized in this paper based on the study objectives and methodology applied. More than half of the reviewed studies on AVs focus on capturing individuals’ behavioral characteristics and perceptions. The second most prevalent category includes studies about individuals’ willingness to pay to use AVs. The reviewed studies were also categorized according to the study population. The paper identifies and classifies attitudinal questions in each survey into different components that may affect behavioral intention to ride in AVs and provides information on specific hypotheses that were set in the studies. Moreover, a discussion of the benefits, barriers/concerns, and opportunities related to the deployment of AVs is presented. The paper concludes by summarizing the lessons learned and outlining the research gaps.

What have we learned? A review of stated preference and choice studies on autonomous vehicles

Although automated vehicles (AVs) could offer a potentially effective solution to improving road safety, the benefit associated with AVs can be realized only when the public intend to use them. While some efforts have been made to understand why people would use AVs, few of them have investigated the role of social and personal factors in AV acceptance. The present study aimed to fill in this research gap. An AV acceptance model was proposed by extending the Technology Acceptance Model (TAM) with social and personal factors, i.e., initial trust, social influence, and the Big Five personality and sensation seeking traits. The validity of the proposed model was confirmed with a questionnaire survey administrated to 647 drivers in China. Results revealed that at the very beginning of AV commercialization, perception factors (i.e., perceived ease of use and perceived usefulness) from the original TAM showed significant influence on users’ intention to use AVs. But more importantly, it was social influence and initial trust that contributed most to explain whether users would accept AVs or not. Some personality traits also played certain roles in AV usage intention. In particular, sensation seekers and those with a higher openness to experience were more likely to trust AVs and had a higher intention to adopt them. In contrast, neurotic people showed a lower level of trust and were less likely to accept AVs. Practically, these findings suggest that promotion of AVs to influential individuals that could help form good social opinions would have significant downstream effects on AV acceptance at the early state of its marketization.

Automated vehicle acceptance in China: social influence and initial trust are key determinants

Automated driving (AD) is one of the most significant technical advances in the transportation industry. Its safety, economic, and environmental benefits cannot be realized if it is not used. To explain, predict, and increase its acceptance, we need to understand how people perceive and why they accept or reject AD technology. Drawing upon the trust heuristic, we tested a psychological model to explain three acceptance measures of fully AD (FAD): general acceptance, willingness to pay (WTP), and behavioral intention (BI). This heuristic suggests that social trust can directly affect acceptance or indirectly affect acceptance through perceived benefits and risks. Using a survey (N = 441), we found that social trust retained a direct effect as well as an indirect effect on all FAD acceptance measures. The indirect effect of social trust was more prominent in forming general acceptance; the direct effect of social trust was more prominent in explaining WTP and BI. Compared to perceived risk, perceived benefit was a stronger predictor of all FAD acceptance measures and also a stronger mediator of the trust-acceptance relationship. Predictive ability of the proposed model for the three acceptance measures was confirmed. We discuss the implications of our results for theory and practice.

Public Acceptance of Fully Automated Driving: Effects of Social Trust and Risk/Benefit Perceptions

Self-driving vehicles (SDVs) promise to considerably reduce traffic crashes. One pressing concern facing the public, automakers, and governments is "How safe is safe enough for SDVs?" To answer this question, a new expressed-preference approach was proposed for the first time to determine the socially acceptable risk of SDVs. In our between-subject survey (N = 499), we determined the respondents' risk-acceptance rate of scenarios with varying traffic-risk frequencies to examine the logarithmic relationships between the traffic-risk frequency and risk-acceptance rate. Logarithmic regression models of SDVs were compared to those of human-driven vehicles (HDVs); the results showed that SDVs were required to be safer than HDVs. Given the same traffic-risk-acceptance rates for SDVs and HDVs, their associated acceptable risk frequencies of SDVs and HDVs were predicted and compared. Two risk-acceptance criteria emerged: the tolerable risk criterion, which indicates that SDVs should be four to five times as safe as HDVs, and the broadly acceptable risk criterion, which suggests that half of the respondents hoped that the traffic risk of SDVs would be two orders of magnitude lower than the current estimated traffic risk. The approach and these results could provide insights for government regulatory authorities for establishing clear safety requirements for SDVs.

How Safe Is Safe Enough for Self-Driving Vehicles?

The autonomous vehicle (AV) is expected to dramatically increase road safety. Understanding the public’s initial perceptions and acceptance of AV is imperative because these aspects are likely to d...

Run Yang

Papers

Public Acceptance of Fully Automated Driving: Effects of Social Trust and Risk/Benefit Perceptions

How Safe Is Safe Enough for Self-Driving Vehicles?

Evaluating Initial Public Acceptance of Highly and Fully Autonomous Vehicles