Consequences of autonomous vehicles: Ambivalent expectations and their impact on acceptance

Connected and autonomous vehicles (CAVs) are within reach of widespread deployment on public roads, but public perceptions are ambivalent. The objective of the present research was to assess expectations about the consequences of CAV introduction. These expectations should explain CAV acceptance, but their relative importance is poorly understood. We conducted a survey with a representatively drawn panel sample (N = 529) from France, Germany, Italy, and the UK. The survey consisted of a large item pool of expected consequences from CAV introduction, as well as general and affective evaluation of CAVs, ease of use, and behavioral intention to use CAVs. Exploratory factor analysis revealed four facets of expected consequences: road safety, privacy, efficiency and ecological sustainability. On average, expectations were mostly positive for ecological sustainability and safety, but negative for privacy. At the same time, substantial variance existed between respondents and between countries. For safety and efficiency, improvement was expected by a third of respondents, while another third expected worsening. Respondents from Italy expected more positive consequences for safety, while respondents from both France and Germany expected more negative consequences for privacy. To different degrees, all four facets predicted the intention to use CAVs in a structural equation model, primarily via affective evaluations. For policy makers, manufacturers, and service providers, understanding the trade-offs inherent to different CAV solutions will be central to ensure citizens’ needs are respected.     

Situational influencing factors on understanding cooperative actions in automated driving

Cooperative interacting vehicles are a promising approach in the context of automated driving. To ensure understanding and acceptance of such systems, the underlying mechanisms of human cooperation in the context of traffic must be understood. In a driving simulator study, we investigated how situational factors influence cooperative behaviour in a lane change situation on a two-lane German highway during automated driving in the left lane. When another car in the right lane was approaching a slower truck, participants (N = 32) were asked by an automated system to either accelerate, decelerate or maintain speed. The driver’s scope of action, the situation’s criticality for the lane-changing vehicle and the display of intention to change lanes were manipulated. A hierarchical multinomial logistic regression revealed that a wider scope of action, a higher situation’s criticality and signalling the intention to change lanes positively influenced cooperative behaviour by accelerating and decelerating. These results might be applied to design user-centred automated cooperatively interacting vehicles.     

Do drivers accept cooperative behavior of their automated vehicle on highways?

Automated cooperatively interacting vehicles will change the future of traffic completely. Such vehicles will be capable of planning and carrying out maneuvers based on vehicle-to-vehicle and vehicle-to-infrastructure communication, enabling a safer driving experience. However, this gain of safety will only be effective if drivers use and accept the decisions made by advanced automated technology. Especially when drivers are cognitively distracted, new strategies might be necessary, e.g., by further explaining the reason for a cooperative decision.In a driving simulator study, we investigated the acceptance of lane change maneuvers in cooperative situations carried out by an automated vehicle on a two-lane German highway. When the automated system detected a potential lane change ahead, it carried out one of three possible maneuvers: accelerate, decelerate, or maintain speed. Participants (N = 20) were asked whether they accepted the system’s behavior either while being cognitively distracted or in an attentive state. Thus, we investigated the influence of a cognitively demanding secondary task and, in addition, further situational characteristics (Scope of action, Criticality for the lane-changing vehicle, Display of intention) on the acceptance towards the system’s behavior. Moreover, participants had to rate the perceived situation’s criticality.Results underlined the importance of explicit indication of the intention to change lanes. Furthermore, increased cognitive load led to a higher acceptance of the defensive system behavior. This study contributes to the development of a user-centered interface and interaction strategy for cooperative interacting vehicles.     

Public acceptance of connected vehicles: An extension of the technology acceptance model

To examine the public acceptance of connected vehicles (CVs), this study developed a novel connected vehicle acceptance model (CVAM) extending the technology acceptance model (TAM). The model was built based on a questionnaire survey of 2400 US adults. Perceived data privacy and security associated with the technology was found to shape the trust, attitude, and behavioral intention to use CVs, in addition to the predictors of original TAM: perceived usefulness and ease of use. Results revealed that trust mediates the effect of perceived data privacy and security on CV acceptance, though its directional relationship with perceived usefulness and perceived ease of use is unclear. Socio-demographic and other characteristics of respondents associated with CV acceptance and its predictors were explored. A number of theoretical and practical implications of the study findings are discussed.     

Impressions after an automated mobility experience: An acceptance study

This paper presents people’s impressions after a demonstration of a fully robotized electric vehicle in Rambouillet, a peri-urban area connected with rural sections in France. 155 participants experienced a 6.5 km ride that included driving in a narrow two-way road, negotiating roundabouts, traversing a tunnel and interacting with other users of the road such as vehicles, pedestrians and cyclists. 298 impressions from 114 respondents were collected from a written questionnaire and open interviews. Users’ comments were classified in categories as follows: system capabilities, purpose and benefits, travel destination and encouraging remarks. Major results show that participants felt safe, where only 1.01% of comments denoted apprehension despite the increase of speed (up to 50 km/h) with respect to other studies. Users’ were satisfied with the automated vehicle (AV) performance, where only 3.5% criticized the system, denoting they expected a higher level of autonomy. 22% of users mentioned train stations as useful application (i.e. potential destination) for an automated on-demand mobility service. More than 200 comments were related to travel destinations, pointing towards various scenarios where an AV service could be useful for them. Conclusions show that realistic vehicle speed increases passenger acceptance of automated vehicles, increasing system confidence with respect to previous studies in the field.     

Different level automation technology acceptance: Older adult driver opinion

The increase in the number of older adult drivers in developed countries has raised safety concerns due to the decline in their sensory, motor, perceptual, and cognitive abilities which can limit their driving capabilities. Their driving safety could be enhanced by the use of modern Automated Driver Assistance Systems (ADASs) and might totally resolved by full driving automation. However, the acceptance of these technologies by older adult drivers is not yet well understood. Thus, this study investigated older adult drivers’ intention to use six ADASs and full driving automation through two questionnaires with 115 and 132 participants respectively in Rhode Island, USA. A four-dimensional model referred to as the USEA model was used for exploring older adult drivers’ technology acceptance. The USEA model included perceived usefulness, perceived safety, perceived ease of use, and perceived anxiety. Path Analysis was applied to evaluate the proposed model. The results of this study identified the important factors in older adult drivers’ intention to use ADASs and full driving automation, which could assist stakeholders in improving technologies for use by older drivers.     

Negative attitudes towards cyclists influence the acceptance of an in-vehicle cyclist detection system

The shift towards automation and safer vehicles will increasingly involve use of technological advancements such as Advanced Driver Assistance Systems (ADAS). Nevertheless, these technologies need to meet users’ perceived needs to be effectively implemented and purchased. Based on an updated version of the Technology Acceptance Model (TAM), this study analyses the main determinants of drivers’ intention to use an ADAS aimed at detecting cyclist and preventing potential collision with them through an auto-braking system. Even if the relevance of perceived usefulness, perceived ease of use and trust on the acceptance of a new system has been already discussed in literature, we considered the role of an external variable such as attitudes towards cyclists in the prediction of an ADAS aimed to improve the safety of cyclists. We administered a questionnaire measuring negative attitudes towards cyclists, trust, perceived usefulness, perceived ease of use and the behavioural intention to use the system to 480 Italian drivers. Path analysis using Bayesian estimation showed that perceived usefulness, trust in the system, and negative attitudes towards cyclists have a direct effect on the intention to use the ADAS. Considering the role of attitudes towards other road users in the intention to use new ADAS aimed to improve their safety could foster the user’s acceptance, especially for those people who express a negative representation of cyclists and are even more unlikely to accept the technology.     

Safety at first sight? – Manual drivers’ experience and driving behavior at first contact with Level 3 vehicles in mixed traffic on the highway

Soon, manual drivers will interact with conditionally automated vehicles (CAVs; SAE Level 3) in a mixed traffic on highways. As of yet, it is largely unclear how manual drivers will perceive and react to this new type of vehicle. In a driving simulator study with N = 51 participants aged 20 to 71 years (22 female), we examined the experience and driving behavior of manual drivers at first contact with Level 3 vehicles in four realistic driving scenarios (highway entry, overtaking, merging, introduction of a speed limit) that Level 3 vehicles may handle alone once their operational domain extends beyond driving in congested traffic. We also investigated the effect of an external marking via a visual external human–machine interface (eHMI), with participants being randomly assigned to one of three experimental groups (none, correct, incorrect marking). Participants experienced each driving scenario four times, twice with a human-driven vehicle (HDV), and twice with a CAV. After each interaction, participants rated perceived driving mode of the target vehicle as well as perceived safety and comfort. Minimum time headways between participants and target vehicles served as an indicator of safety criticality in the interactions. Results showed manual driver can distinguish CAVs from HDVs based on behavioral differences. In all driving scenarios, participants rated interactions with CAVs at least as safe as interactions with HDVs. The driving data analysis showed that manual driver interactions with CAVs were largely uncritical. However, the CAVs’ strict rule-compliance led to short time headways of following manual drivers in some cases. The eHMI used in this study neither affected the subjective ratings of the manual drivers nor their driving behavior in mixed traffic. Thus, the results do not support the use of eHMIs on the highway, at least not for the eHMI design used in this study.     

Manual drivers’ experience and driving behavior in repeated interactions with automated Level 3 vehicles in mixed traffic on the highway

In the near future, conditionally automated vehicles (CAVs; SAE Level 3) will travel alongside manual drivers (≤ SAE level 2) in mixed traffic on the highway. It is yet unclear how manual drivers will react to these vehicles beyond first contact when they interact repeatedly with multiple CAVs on longer highway sections or even during entire highway trips. In a driving simulator study, we investigated the subjective experience and behavioral reactions of N = 51 manual drivers aged 22 to 74 years (M = 41.5 years, SD = 18.1, 22 female) to driving in mixed traffic in repeated interactions with first-generation Level 3 vehicles on four highway sections (each 35 km long), each of which included three typical speed limits (80 km/h, 100 km/h, 130 km/h) on German highways. Moreover, the highway sections differed regarding the penetration rate of CAVs in mixed traffic (within-subjects factor; 0%, 25%, 50%, 75%). The drivers were assigned to one of three experimental groups, in which the CAVs differed regarding their external marking, (1) status eHMI, (2) no eHMI, and (3) a control group without information about the mixed traffic. After each highway section, drivers rated perceived safety, comfort, and perceived efficiency. Drivers were also asked to estimate the penetration rate of CAVs on the previous highway section. In addition, we analyzed drivers’ average speed and their minimum time headways to lead vehicles for each speed zone (80 km/h, 100 km/h, 130 km/h) as well as the percentage of safety critical interactions with lead vehicles (< 1 s time headway). Results showed that manual drivers experienced driving in mixed traffic, on average, as more uncomfortable, less safe and less efficient than driving in manual traffic, but not as dangerous. A status eHMI helps manual drivers identify CAVs in mixed traffic, but the eHMI had no effect on manual drivers’ subjective ratings or driving behavior. Starting at a level of 25% Level 3 vehicles in mixed traffic, participants' average speed decreased significantly. At the same time, the percentage of safety critical interactions with lead vehicles increased with an increasing penetration rate of CAVs. Accordingly, additional measures may be necessary in order to at least keep the existing safety level of driving on the highway.     

“Do I really need it?”: An explorative study of acceptance and usage of in-vehicle technology

The vehicle is increasingly equipped with additional technology assisting and entertaining the driver. To improve the systems and increase their usage, it is important to understand what influences the acceptance of technology in the vehicle. An online survey was conducted assessing which systems drivers own and use in their vehicles today. For the equipped systems, the reasons why N = 304 drivers do not use their in-vehicle technology were qualitatively explored. An inductive content analysis revealed 13 categories in total. The three categories “Need”, “Context and Task”, and “Reliability” were associated with Perceived Usefulness while “Increased Effort” and “Aversion” were associated with Perceived Ease of Use (Venkatesh, 2000). In addition, the influencing factors are further extended with the “Preference for Own Action”, “Distrust”, “Safety”, “Knowledge”, and “Habit”. The findings reveal subjectively important antecedents of the acceptance of in-vehicle technology and provide new insights, especially on usage barriers. An Integrated Acceptance Model (IAM) is derived from the identified categories to inform future research and facilitate a holistic view on factors influencing technology acceptance.     

Kano model of autonomous driving user acceptance according to driver characteristics: A survey study

The development of advanced technology has revolutionized human life. In this regard, autonomous driving, a core technology currently being developed, is changing rapidly. In addition to improving technology, the acceptance of technology users must be secured. Most relevant studies conducted hitherto have involved evaluation using acceptance elements defined based on the technology acceptance model and the unified theory of acceptance and use of technology. In this study, 21 elements associated with the acceptance of autonomous driving are defined. The Kano model is used to classify the acceptance elements into five attributes and to propose guidelines for improving acceptance. Driver characteristics are classified based on four human factors, which are used to investigate differences in acceptance between groups. A Google survey and fieldwork were completed by 187 participants. Contrary to previous studies, no significant gender differences are observed in the current study. In terms of age, many obstacles are encountered in securing autonomous driving acceptance from the elderly driver group. Additionally, a more conservative tendency is indicated by people with more driving experience. The results of this study reveal important points for identifying elements that hinder future sustainability and commercialization of autonomous driving, thereby facilitating its further technological development.     

Drink driving during the COVID-19 pandemic

Due to the COVID-19 pandemic, static roadside random breath testing (RBT) was temporarily suspended between 16 March and 12 June 2020 in Queensland, Australia. In addition to restrictions on travel and social interactions, this provided a unique opportunity to examine changes in drink-driving behaviour during and after a reduction in RBT operations in the community. Three cross-sectional surveys were disseminated at different time points to examine these differences. Over three surveys, 1193 Queensland licensed drivers aged 18 years and over (M = 36.9, SD = 16.7) responded. While overall drink driving decreased over the three survey periods, there were groups where drink driving, or the intention to drink drive, increased over the same period. The overall decrease could be expected as a result of community restrictions on socialising and travel behaviours. In each of the surveys, prior engagement in drink driving was the strongest predictor of intention to increase future engagement and actual engagement. These drink drivers were more likely aged 18–24 years, male, and held restricted licensure. Notably, a small number of participants who reported drink driving, and/or intention to drink drive during the survey period, reported not having engaged in this behaviour previously. This suggested an increased likelihood of drink drivers experiencing punishment avoidance which may promote future engagement in this behaviour. Despite a decrease in social opportunities to drink, and the suspension of highly visible roadside breath testing sites, drink driving persisted. This research highlights the importance of RBTs as a general deterrent for drink driving.     

Driving next to automated vehicle platoons: How do short time headways influence non-platoon drivers’ longitudinal control?

Advanced driver assistance systems (ADAS) are taking over an increasing part of the driving task and are supporting the introduction of semi- and fully automated vehicles. As a consequence, a mixed traffic situation is developing where vehicles equipped with automated systems taking over the lateral and longitudinal control of the vehicle will interact with unequipped vehicles (UV) that are not fitted with such automated systems. Different forms of automation are emerging and it appears that regardless of which form is going to become popular on our roads, there is a consensus developing that it will be accompanied by a reduction in time headway (THW). The present simulator study examined whether a ‘contagion’ effect from the short THW held in platoons on the UV drivers would occur. Thirty participants were asked to follow a lead vehicle (LV) on a simulated motorway in three different traffic conditions: surrounding traffic including (1) platoons with short following distance (THW = 0.3 s), (2) large following distance (THW = 1.4 s) or (3) no platoons at all. Participants adapted their driving behaviour by displaying a significant shorter average and minimum THW while driving next to a platoon holding short THWs as when THW was large. They also spent more time keeping a THW below a safety threshold of 1 s. There was no carryover effect from one platoon condition to the other, which can be interpreted as an effect that is not lasting in time. The results of this study point out the importance of examining possibly negative behavioural effects of mixed traffic on UV drivers.     

Public opinion on automated driving: Results of an international questionnaire among 5000 respondents

This study investigated user acceptance, concerns, and willingness to buy partially, highly, and fully automated vehicles. By means of a 63-question Internet-based survey, we collected 5000 responses from 109 countries (40 countries with at least 25 respondents). We determined cross-national differences, and assessed correlations with personal variables, such as age, gender, and personality traits as measured with a short version of the Big Five Inventory. Results showed that respondents, on average, found manual driving the most enjoyable mode of driving. Responses were diverse: 22% of the respondents did not want to pay more than $0 for a fully automated driving system, whereas 5% indicated they would be willing to pay more than $30,000, and 33% indicated that fully automated driving would be highly enjoyable. 69% of respondents estimated that fully automated driving will reach a 50% market share between now and 2050. Respondents were found to be most concerned about software hacking/misuse, and were also concerned about legal issues and safety. Respondents scoring higher on neuroticism were slightly less comfortable about data transmitting, whereas respondents scoring higher on agreeableness were slightly more comfortable with this. Respondents from more developed countries (in terms of lower accident statistics, higher education, and higher income) were less comfortable with their vehicle transmitting data, with cross-national correlations between ρ = −0.80 and ρ = −0.90. The present results indicate the major areas of promise and concern among the international public, and could be useful for vehicle developers and other stakeholders.     

Evidence that implementation intentions reduce drivers’ use of mobile phones while driving

Implementation intentions are IF-THEN plans that have the potential to reduce mobile phone use while driving and thus contribute towards the prevention of road traffic crashes. We tested whether an intervention, designed to promote the formation of implementation intentions, could reduce drivers’ use of mobile phones. A randomized controlled design was used. The participants (N = 136) were randomised to an implementation intention or a control condition. Self-report questionnaires were administered to all participants at both pre- and one-month post-intervention to measure the use of mobile phones while driving, goal intentions and the theoretically derived motivational pre-cursors of goal intentions (attitudes, subjective norm and perceived behavioural control). Immediately following the pre-intervention questionnaire, the participants in the implementation intention condition (n = 67) were given a volitional help sheet, which asked them to form implementation intentions by specifying target driving situations that tempted them the most to use a mobile phone and linking them with goal-directed responses that could be used to resist the temptation. The participants in the control condition (n = 69) were asked to specify target situations that tempted them the most to use a mobile phone while driving and to generally try to avoid using a mobile phone in those situations. One-month post-intervention, the participants in the implementation intention condition reported using a mobile phone less often while driving in their specified target driving situations than did the participants in the control condition. As expected, no differences were found between the conditions in the reported frequency of mobile phone use in unspecified driving situations, goal intentions or any motivational pre-cursor of goal intentions. The implementation intention intervention that was tested in this study is a potentially effective tool for reducing mobile phone use while driving in target driving situations, where behaviour-change is most needed.     

Sharing the road with autonomous vehicles: A qualitative analysis of the perceptions of pedestrians and bicyclists

Public perception assessment is important for gaining a better understanding of the acceptance of autonomous vehicles (AVs) and identifying potential ways to resolve public concerns. This study investigated how pedestrians and bicyclists perceived AVs based on their knowledge and road sharing experiences, applying a combined inductive and deductive data analysis approach. Survey responses of pedestrians and bicyclists in Pittsburgh, Pennsylvania, USA collected by Bike Pittsburgh (BikePGH) in 2019, were analyzed in this research. AVs following traffic rules appropriately and AVs driving safer than the human drivers were the most notable positive perceptions towards AVs. Pedestrians and bicyclists showed comparatively fewer negative perceptions towards AVs than positive perceptions. Negative perceptions mostly included a lack of perceived safety and comfort around AVs and trust in the AV technology. Respondents also concerned about AV technology issues (e.g., slow and defensive driving, disruptive maneuver), while sharing the road with AVs. Perceptions of the respondents were significantly influenced by their views on AV safety, familiarity with the technology, the extent respondents followed AV on the news, and household automobile ownership. Regulating AV movement on roadways, developing safety assessment guidelines, and controlling oversights of improper practices by AV companies were the major suggestions from the survey participants. Findings of this study might help AV companies to identify potential improvement needed in AV technology to increase pedestrians and bicyclists acceptance, and policymakers to develop policy guidelines to ensure safe road sharing among pedestrians, bicyclists, and AVs.     

Using the UTAUT2 model to explain public acceptance of conditionally automated (L3) cars: A questionnaire study among 9,118 car drivers from eight European countries

We investigated public acceptance of conditionally automated (SAE Level 3) passenger cars using a questionnaire study among 9,118 car-drivers in eight European countries, as part of the European L3Pilot project. 71.06% of respondents considered conditionally automated cars easy to use while 28.03% of respondents planned to buy a conditionally automated car once it is available. 41.85% of respondents would like to use the time in the conditionally automated car for secondary activities. Among these 41.85%, respondents plan to talk to fellow travellers (44.76%), surf the internet, watch videos or TV shows (44%), observe the landscape (41.70%), and work (17.06%). The UTAUT2 (Unified Theory of Acceptance and Use of Technology) was applied to investigate the effects of performance and effort expectancy, social influence, facilitating conditions, and hedonic motivation on the behavioural intention to use conditionally automated cars. Structural equation analysis revealed that the UTAUT2 can be applied to conditional automation, with hedonic motivation, social influence, and performance expectancy influencing the behavioural intention to buy and use a conditionally automated car. The present study also found positive effects of facilitating conditions on effort expectancy and hedonic motivation. Social influence was a positive predictor of hedonic motivation, facilitating conditions, and performance expectancy. Age, gender and experience with advanced driver assistance systems had significant, yet small (<0.10), effects on behavioural intention. The implications of these results on the policy and best practices to enable large-scale implementation of conditionally automated cars on public roads are discussed.     

Please stop now,automated vehicle! – Passengers aim to avoid risk experiences in interactions with a crossing vulnerable road user at an urban junction

The success of highly automated vehicles (HAVs; SAE Level 4) will depend to a large extent on how well they are accepted by their future passengers. This is especially true for the interaction of these vehicles with other human road users in mixed traffic. In future urban traffic, passengers of HAVs will observe from a passive position how the automated system resolves space-sharing conflicts with crossing vulnerable road users (VRUs; e.g., pedestrians and cyclists) at junctions. For one such crossing-paths conflict, we investigated when passengers would want the HAV to start braking and how much perceived risk passengers accept in the interaction of their vehicle with VRUs. To this end, we conducted 1) an online video study (N = 118), 2) a driving simulator study (N = 28), and 3) a human&vehicle-in-the-loop (Hu&ViL) study at a test site (N = 10). We varied the speed of the HAV (30 km/h vs. 50 km/h), the type (cyclist vs. pedestrian), and crossing direction of the VRU (left vs. right). During the approach to the junction, passengers' task was to trigger the HAV's braking maneuver, in a first trial at the point they considered ideal and in a second trial at the last point they still considered safe enough to decelerate and come to a stop at the stop line. For each braking maneuver, we analyzed the HAV’s distance and time-to-arrival (TTA) to the VRU at braking onset, as well as passengers’ perceived risk in the VRU interaction. The results showed that most passengers preferred harmless interactions with VRUs (at the ideal braking onset time), and accepted unpleasant, but not dangerous interactions at most (at the last acceptable braking onset time). Methodologically, the results were very similar in the three different environments (online, driving simulator, real vehicle). These results clearly show that, in addition to the technical considerations of safe automated driving, passengers’ perception and evaluation of HAV driving behavior should also be taken into account to achieve a satisfying level of acceptance of these vehicles.     

Public acceptance of driver state monitoring for automated vehicles: Applying the UTAUT framework

Driver state monitoring (DSM) systems aim to measure driver/occupant state, considering factors such as fatigue, workload, attentiveness, and wellbeing. They are influential for some vehicles on the road today, but as we move towards higher levels of automation their use is expected to become even more important. Uncertainty around public perception of these systems is a potentially limiting factor, with potential concerns for privacy, data storage, perceived utility, and reliability amongst other things posing potential threats. A survey was conducted based on the Universal Theory of Acceptance and Use of Technology (UTAUT) framework, which collected 311 individual responses to explore the acceptability of this technology in specific relation to conditionally and partially automated vehicles. This paper identified that the factors of effort expectancy, performance expectancy, social influence, and attitudes towards using new technology are all positively related to overall behavioural intention, whereas anxiety is negatively related. Other demographic influences are discussed, and recommendations made for future research and marketing/implementation of DSM systems to ensure acceptance by future users.