Autonomous Vehicle User Research Articles

Relax on the way to work or work on the way to relax? Influences of vehicle interior on travel time perceptions in autonomous vehicles

The impending arrival of autonomous vehicle (AV) technology has the potential to transform how individuals perceive time spent travelling. By removing the need to drive and pay attention to the road, AV users could perform other activities, including those for work or leisure. As a result, AVs are expected to lower the burden of travel and, therefore, the value of travel time (VOTT). Despite the significant impacts that AVs may have on individuals’ choices and the transportation system, few have studied their impacts on travel time perceptions, and even fewer have examined the extent to which these impacts will vary depending on the types of tasks that can be performed within an AV. This study uses stated preference data collected in Fall 2022 to develop mode choice models and subsequently quantify how the availability of three types of AV: privately-owned, exclusive, and pooled AV may shift perceived travel times in the Greater Toronto and Hamilton Area. The error-component mixed logit models highlight the cross-nesting between privately-owned AVs and driving. In addition, this study is the first in Canada to distinguish the VOTT reductions by AV type, trip purpose, and interior description (which caters to different tasks). VOTT reductions as large as 42% less than driving a conventional vehicle were estimated. The results of this study provide additional empirical evidence for AV VOTT reductions (particularly in the Canadian context) and can be used to help craft policies in preparation for the arrival of AVs.

Parking Lot Pricing Optimization Strategy Considering Autonomous Vehicle User Choice Behavior

Parking Lot Pricing Optimization Strategy Considering Autonomous Vehicle User Choice Behavior

Lived Experiences of People with and without Disabilities across the Lifespan on Autonomous Shuttles

As an emerging, alternative mode of transportation, an in-depth understanding of autonomous shuttle (AS) experiences among all age groups, with and without disabilities, may impact acceptance and adoption of the AS, shape industry guidelines, and impact public policy. Therefore, this study analyzed qualitative data from older (n = 104), younger, and middle-aged (n = 106) adults and people with disabilities (n = 42). The data were obtained by asking participants four open-ended questions from an Autonomous Vehicle User Perception Survey. The result revealed seven themes (Safety, Ease of Use, Cost, Availability, Aging, AS Information, and Experience with AS) for older, younger, and middle-aged adults and six themes (all of the previously mentioned except for Aging) for people with disabilities. Frequency counts indicated priority attention, among all groups, to Safety and Ease of Use. This study provides valuable information pertaining to the experiences, concerns, and motivations of all potential users across age groups and disabilities—and may inform policymakers and industry partners to address their needs more adequately. These findings may contribute to improving and enhancing AS programming, design, and deployment in a safer, accessible, affordable, and tailored way.

Evaluation of an Autonomous Vehicle User Interface for Sensory Impaired Users

Evaluation of an Autonomous Vehicle User Interface for Sensory Impaired Users

Promoting Veteran-Centric Transportation Options through Exposure to Autonomous Shuttles

Veterans face difficulties accessing vital health and community services, especially in rural areas. Autonomous vehicles (AVs) can revolutionize transportation by enhancing access, safety, and efficiency. Yet, there is limited knowledge about how Veterans perceive AVs. This study fills this gap by assessing Veterans’ AV perceptions before and after exposure to an autonomous shuttle (AS). Using a multi-method approach, 23 participants completed pre- and post-AS Autonomous Vehicle User Perception Survey (AVUPS), with 10 participants also taking part in post-AS focus groups. Following exposure to the AS, differences were observed for three out of the four AVUPS domains: an increase in Intention to Use (p < 0.01), a decrease in Perceived Barriers (p < 0.05), and an increase in Total Acceptance (p = 0.01); Well-being remained unchanged (p = 0.81). Feedback from focus groups uncovered six qualitative themes: Perceived Benefits (n = 70), Safety (n = 66), Shuttle Experience (n = 47), AV Adoption (n = 44), Experience with AVs (n = 17), and Perception Change (n = 10). This study underscores AVs’ potential to alleviate transportation challenges faced by Veterans, contributing to more inclusive transportation solutions. The research offers insights for future policies and interventions aimed at integrating AV technology into the transportation system, particularly for mobility-vulnerable Veterans in rural and urban settings.

Understanding the antecedents of hedonic motivation in autonomous vehicle technology acceptance domain: A cross-country analysis

The literature on autonomous vehicle (AV) acceptance highlights the significance of hedonic motivation in AV adoption. Numerous studies empirically confirm hedonic motivation as either the most or one of the most influential factors in the acceptance of AV. This fact calls for a need to achieve a wider understanding of the potential AV users’ perceived enjoyment (i.e., hedonic motivation). To this end, this study investigates the antecedents of hedonic motivation in the AV technology acceptance domain. The partial least square structural equations modeling approach was applied to analyze the data collected from 1823 respondents from 11 countries via an online survey. The developed hypotheses are examined for the entire sample, as well as separately for the Global North (GN) countries' sample, Global South (GS) countries' sample, and each individual country through a cross-country analysis. The results for the entire sample indicate that social influence is the strongest predictor of hedonic motivation, consistent with the findings of the GN sample. However, in the GS sample, self-efficacy emerges as the strongest predictor of hedonic motivation. Perceived safety is the second strongest predictor of hedonic motivation for both the GN and GS samples, highlighting its importance in relation to the perceived enjoyment of PAV. Trust does not significantly contribute to hedonic motivation, while the enjoyment of driving conventional cars has a small negative impact on hedonic motivation in the GS sample. The cross-country analysis reveals general patterns in the findings of the GN and GS samples, while highlighting a few exceptions. The results of the multi-group moderation analysis highlight the significant impact of the respondents' geographical distribution (GN vs GS) on their perceived enjoyment of PAV. Additionally, the analysis indicates that female respondents who enjoy driving conventional cars are less likely to perceive PAV as enjoyable compared to male participants who enjoy driving conventional cars.

A day-to-day dynamic model for mixed traffic flow of autonomous vehicles and inertial human-driven vehicles

Autonomous vehicle (AV) technology is likely to bring unprecedented change to existing transport systems due to its potential to enhance safety and efficiency. Nevertheless, before entering a fully AV era, human-driven vehicles (HVs) and AVs are expected to coexist for a long time, during which HVs and AVs could behave differently in route choice. On the one hand, compared with HVs, AVs can acquire more comprehensive traffic information and also process the information more swiftly. On the other hand, HV users may habitually maintain a satisfactory path but not necessarily choose the optimal one to the same destination to save cognitive during daily trips. AV users, however, may not show such behavior on account of the advantages of AVs. To capture these features in the coexistent period, understand their effects on the evolution of daily traffic flow, and provide managerial insights, this study presents a deterministic discrete time day-to-day dynamic assignment model for the mixed HV and AV traffic flows. In which both AV users and HV users choose their path according to their perception, AV users have better perceptual ability than HV users, and bounded behavioral inertia is first introduced to depict the travel behavior that HV users habitually maintain a satisfactory path during daily trips. The existence and uniqueness of the fixed-point and the convergence conditions of the proposed dynamic model are demonstrated and given. Theoretical results are verified in a simple two-path network, the Nguyen-Dupuis network, and the Sioux Falls network, respectively. The interactions between AVs and HVs are explored in the three numerical examples. Our research indicates that the introduction of AVs can reduce the overall travel time of a transport system and increase road capacity. Bounded behavioral inertia of HV users can maintain more HV flows on a better route, brings a more balanced distribution of travel flow on different paths, and facilitate the stability of a whole transport system.

Experience of drivers of all age groups in accepting autonomous vehicle technology

Autonomous vehicles (AVs) may benefit the health and safety of drivers across the driving lifespan, but perceptions of drivers are not known. Lived experiences of drivers exposed to AVs in combination with surveys, can more accurately reveal their perceptions. We quantified facilitators and barriers from data collected in older (N = 104) and younger drivers (N = 106). Perceptions were assessed via Autonomous Vehicle User Perception Survey (AVUPS) subscales (i.e., intention to use, barriers, well-being, and acceptance) pertaining to group exposure (simulator first [SF] or autonomous shuttle first [ASF]). We quantified the effects of group, time, and group × time interaction. Multiple linear regressions identified predictors (e.g., optimism, ease of use, life space, driving exposure, and driving difficulty, age, gender, race) of the AVUPS subscales. The regression analyses indicated that optimism and ease of use positively predicted intention to use, barriers, well-being, and the total acceptance score. Driving difficulty significantly predicted barriers, whereas miles driven negatively predicted well-being. The regression results indicated that predictors of user acceptance of AV technology included age, race, optimism, ease of use, with 33.6% of the variance in acceptance explained. The findings reveal foundational information about driver acceptance, intention to use, barriers, and well-being related to AVs. New knowledge pertains to how demographics, optimism, ease of use, life space, driving exposure, and driving difficulty inform AV acceptance. We provided strategies to inform city planners and other stakeholders on improving upon deployment practices of AVs.

The Elderly Acceptance of Autonomous Vehicle Services in Beijing, China

China's population has been ageing fast at an accelerated pace since the 21st century, while the mobility and convenience of travel for the elderly were weakened by the limited transportation options. Autonomous vehicles, an emerging mode of transport, provide flexibility for the elderly, provided that the rapid and widespread this new mode of transportation can be accepted by the elderly population. This paper plans to gain insights from the attitudes of the Beijing elderly using autonomous vehicles, perceiving the importance of different types of services. The stated preference survey was conducted over 376 respondents from various generations, which examined their perception of autonomous vehicles after their retirement. An ordered logistics model used in this paper shows that people who use emerging transportation modes (e.g., car-sharing and ride-hailing) are more likely to use autonomous vehicle services. Accessing shopping activities and medical care is the two crucial purposes for the elderly autonomous vehicle users. Additionally, the clustering and correlation analysis indicate that people with a higher willingness to use autonomous vehicles are the earliest target users of these services. However, living with children and running errands in short distances by bicycle and walk, the elderly in the early generation hardly adopts autonomous vehicle services after their retirement.

COVID-19 and public support for autonomous technologies-Did the pandemic catalyze a world of robots?

By introducing a novel risk to human interaction, COVID-19 may have galvanized interest in uses of artificial intelligence (AI). But was the pandemic a large enough catalyst to change public attitudes about the costs and benefits of autonomous systems whose operations increasingly rely on AI? To answer this question, we use a preregistered research design that exploits variation across the 2018 and 2020 waves of the CCES/CES, a nationally representative survey of adults in the United States. We compare support for autonomous cars, autonomous surgeries, weapons, and cyber defense pre- and post-the beginning of the COVID-19 pandemic. We find that, despite the incentives created by COVID-19, the pandemic did not increase support for most of these technologies, except in the case of autonomous surgery among those who know someone who died of COVID-19. The results hold even when controlling for a variety of relevant political and demographic factors. The pandemic did little to push potential autonomous vehicle users to support adoption. Further, American concerns about autonomous weapons, including cyber defense, remain sticky and perhaps exacerbated over the last two years. These findings suggest that the relationship between the COVID-19 pandemic and the adoption of many of these systems is far more nuanced and complex than headlines may suggest.

A practical role-based approach for autonomous vehicle moral dilemmas

Autonomous vehicle moral dilemmas matter less for the particular outcomes of potential accidents than for their role in defining the values of the society we wish to live in. Different approaches have been suggested to determine the ethical settings that autonomous vehicles should be implemented in and identify the legitimate agents for making such decisions. Most of these, however, fail on theoretical grounds, facing severe issues related to moral justifications and compliance to the law, or on practical grounds, being insufficiently universal, action-guiding, or technically viable to be implemented. The analogy with the “trolley problem” has been extensively discussed. However, researchers have rarely tried to adapt this framework to autonomous vehicle cases or investigate how it could be used to address these issues. In doing so, this paper aims to answer the two key problems of autonomous vehicle dilemmas. With regards to the decision-maker, it rejects autonomous vehicle users’ choice-based models, showing the absurdity of both switch of control and adaptative preferences and arguing for common legislator-determined ethical settings. With regards to the decisions themselves, it criticizes both utilitarian views and those based on individuals’ criteria to suggest a deontologist rights-based approach. This allows for the defence of a morally coherent, regulatory compliant, explainable, and easily implementable framework capable of addressing all autonomous vehicle moral dilemma scenarios present in the literature.

Supporting Situational Awareness in VANET Attack Scenarios

The integration of sensors and communication technologies is enabling vehicles to become increasingly intelligent and autonomous. The Internet of Vehicles (IoVs) is built from intelligent vehicles that work collaboratively and interact with the surrounding environment in real time. The underlying communications infrastructure is provided by Vehicular Ad-hoc Networks (VANETs), for vehicle to infrastructure (V2I) and vehicle to vehicle (V2V) communications. The volume of autonomous vehicles (AVs) increases, as well as the level of automation for vehicles. The potential for related incidents and attacks increases as a result. A particular concern is the ability to disseminate alerts and emergency messages effectively and securely via the V2V/V2I nodes, given the diminishing involvement of autonomous vehicle users with the operation of the autonomous vehicles.
 
 With this challenge in mind, this paper investigates the issue of situational awareness for occupants in autonomous vehicles. Building from the concept of VANETs and recognised classification of automation levels, the discussion considers a range of related attack scenarios that could be encountered, each of which illustrates also contexts in which occupants may need to be made aware and take decisions in response. Consideration is then given to resulting support for situational awareness that would be required, particularly highlighting the associated requirements for user responsibility at different levels of automation. The resulting discussion serves to articulate the challenge and serves as a basis for further research to inform the mechanisms to address the resulting requirements.

Design and Evaluation of an Inclusive Autonomous Vehicle User Interface Developed for Persons with Visual Acuity Loss

AbstractThis research focuses on evaluating a user interface (UI) for an autonomous vehicle (AV) with the goal to determine the most suitable layout for persons with visual acuity loss. The testing procedure includes a Wizard of Oz AV for simulating an automated ride. Several participants are included in the study and the visual impairments are simulated by specially designed glasses. The conclusions help to determine the optimal graphic design of the UI that can be independently used by persons with blurred vision. The results can be applied to improve the inclusiveness and ergonomics of vehicle UIs.

A stochastic framework using Bayesian optimization algorithm to assess the network-level societal impacts of connected and autonomous vehicles

Connected and autonomous vehicle technologies are expected to alter transportation systems by enhancements in mobility, safety, and emission reduction. While many studies have investigated the impacts of connected vehicle (CV) and autonomous vehicle (AV) technologies on traffic congestion and emission at the facility level, little is known about these impacts at large scales. Furthermore, different effective parameters associated with these impacts, such as the extra vehicle miles traveled (VMT) induced by AVs, technology cost of these vehicles, and possible reductions in the value of time of AV users, are highly uncertain. The uncertainty of these parameters and its role in assessing the societal impacts of CVs and AVs are not fully explored in the literature. Therefore, this study aims to develop a stochastic framework and an optimization algorithm to find the optimum market shares of CVs and AVs in a mixed traffic environment, consisting of human-driven vehicles without connectivity (HDVs), CVs, and AVs, minimizing the system cost. Emission, travel time, and technology costs are considered as components of the system cost. Thus, the framework combines a traffic simulation tool that considers a mixed fleet of HDVs, CVs, and AVs with heterogeneous drivers (for HDVs and CVs) distributed spatially over the network, along with an emission estimation model, to measure network-wide travel time and emission costs. Many parameters, such as extra VMT produced by AVs, value of time reduction for AV users, and automation cost, are subject to a considerable degree of stochasticity, which is considered by assuming probabilistic distributions for these parameters. A Bayesian Optimization algorithm with heteroskedastic non-stationary Gaussian process model is presented to estimate the optimum market shares of CVs and AVs considering these uncertainties. The stochastic framework and the optimization algorithm are successfully applied to a large-scale network of Chicago. The impacts of different parameters on the optimum CV and AV market shares and the system cost are explored, providing practical insights for policymakers.

Optimal design of differentiated credit charging links in mixed-autonomy transportation networks

In this paper, we investigate a novel driveway management strategy, namely deploying autonomous vehicle/credit charging links in mixed-autonomy transportation networks. Such links are freely accessible for (connected) autonomous vehicles, while charging credits for human-driven vehicles. We formulate the mixed traffic equilibrium as a variational inequality problem, based on which a bi-level programming model is proposed to simultaneously determine the optimal location as well as the credit charge of these links. Then a genetic algorithm is applied to solve the bi-level problem, within which a gradient projection method is used to solve the lower-level problem. Numerical results reveal that deployment of such links further improves the network performance compared with the case where only a tradable credit scheme is implemented. We also find that different strategies are employed to deploy the differentiated charging links under different scenarios, which provides useful insight for the practical implementation. Moreover, we investigate the credit trading market and find that connected autonomous vehicle users are more likely to be the beneficiary in the proposed scheme. In this regard, the proposed scheme also has the potential to propel the market penetration of connected autonomous vehicles.

THE MALAYSIAN PERSPECTIVE ON IMPOSING CIVIL LIABILITIES IN ROAD ACCIDENTS INVOLVING AUTONOMOUS VEHICLE

The advancement of artificial intelligence (AI) technology has become the fundamental catalyst in the research and development of autonomous vehicle (AV). AVs equipped with AI are expected to perform better than humans and forecasted to reduce the number of road accidents. AV will improve humans’ quality of life, such as creating more mobility for the elderly and disabled, increasing productivity, and creating an environmentally friendly system. Despite AV’s promising abilities, reports indicate that AV can go phut, causing road fatalities to the AV user and other road users. The autonomous nature of AV exacerbates the difficulty in determining who is at fault. This article aims to examine the ability of the existing legal framework to identify the person at fault so as to determine the tortious liability in road accidents involving AV. This article demonstrated that the existing legal scheme is insufficient to determine tortious liability in road accidents involving AV. This article explored the possibility of shouldering the liability on the manufacturer, the user, and even on the AV itself. This article also investigated alternative approaches that could be adopted to resolve issues on the distribution of tortious liability in road accidents involving AV. The outcome of this article could contribute to issues relating to the liability of AI.

A Deeper Look at Autonomous Vehicle Ethics: An Integrative Ethical Decision-Making Framework to Explain Moral Pluralism.

The autonomous vehicle (AV) is one of the first commercialized AI-embedded robots to make autonomous decisions. Despite technological advancements, unavoidable AV accidents that result in life-and-death consequences cannot be completely eliminated. The emerging social concern of how an AV should make ethical decisions during unavoidable accidents is referred to as the moral dilemma of AV, which has promoted heated discussions among various stakeholders. However, there are research gaps in explainable AV ethical decision-making processes that predict how AVs’ moral behaviors are made that are acceptable from the AV users’ perspectives. This study addresses the key question: What factors affect ethical behavioral intentions in the AV moral dilemma? To answer this question, this study draws theories from multidisciplinary research fields to propose the “Integrative ethical decision-making framework for the AV moral dilemma.” The framework includes four interdependent ethical decision-making stages: AV moral dilemma issue framing, intuitive moral reasoning, rational moral reasoning, and ethical behavioral intention making. Further, the framework includes variables (e.g., perceived moral intensity, individual factors, and personal moral philosophies) that influence the ethical decision-making process. For instance, the framework explains that AV users from Eastern cultures will tend to endorse a situationist ethics position (high idealism and high relativism), which views that ethical decisions are relative to context, compared to AV users from Western cultures. This proposition is derived from the link between individual factors and personal moral philosophy. Moreover, the framework proposes a dual-process theory, which explains that both intuitive and rational moral reasoning are integral processes of ethical decision-making during the AV moral dilemma. Further, this framework describes that ethical behavioral intentions that lead to decisions in the AV moral dilemma are not fixed, but are based on how an individual perceives the seriousness of the situation, which is shaped by their personal moral philosophy. This framework provides a step-by-step explanation of how pluralistic ethical decision-making occurs, reducing the abstractness of AV moral reasoning processes.

A scientific evaluation of autonomous vehicle user experience on sighted and visually impaired passengers based on FACS (Facial Analysis Coding System) and a user experience questionnaire

IntroductionSignificant progress has been made in the development of autonomous vehicles (AV) in recent years, with an increasing number of trials performed to identify how AVs might function within the existing transport modal choices. With this has come increasing recognition of the multiple benefits that AVs might provide for wider society, not least reduced road congestion and pollution, safer travel, and increased fuel efficiency. Removing the need for a dedicated driver means that costs to use AVs services will be lower; in the case of private AV ownership, the requirement for a driving licence is removed. These attributes are highly advantageous for all passengers, but especially for the disabled community, and in the case of the current project, those with visual impairment. The use of AVs may have significant implications for the independence of this group, impacting on feelings of social and psychological well-being. MethodsIn the current study, 419 people, both VI and sighted, took part in an AV trial (‘Arthur’). Participants' faces (inclusive of guide dogs) were recorded throughout the journey and all participants responded to a questionnaire about their experience during this journey. Recordings were analysed using FACS (Facial Actions Coding System). ResultsResults from the questionnaire indicated an overall positive experience for participants, and reduced anxiety from the time prior to the journey to the end of the journey. FACS analysis showed happiness as the predominant emotion expressed by passengers; a smaller number expressed surprise or fear, these were mainly identified early during the journey or as the result of an emergency stop. ConclusionsThe results from both data sets demonstrate that for a sighted or VI individual, travel in an AV is largely a positive experience and individuals who have a VI would benefit from AV services in the future.

Smart Proactive Caching: Empower the Video Delivery for Autonomous Vehicles in ICN-Based Networks

The recent advances in vehicular communications and networking are bringing self-driving vehicles closer to reality. Once full automation (i.e., levels 4 and 5 as defined in the five levels of autonomous driving) becomes a reality, entertainment services for both drivers and passengers will shift from listening to radios (or music) to watching videos. Hence, how to improve the quality of experience (QoE) for autonomous vehicle (AV) users, and to reduce the network load will become a crucial problem. Information-centric networking (ICN) is seen as one of the potential paradigms for next-generation networks, and could potentially be used for content distribution in vehicular networks. Caching, an important feature in ICN-based networks, is an efficient way to reduce network load, and to improve QoE for users. However, traditional reactive caching approaches are inefficient for AV users due to the high delay caused by their high speed. In this paper, we propose a novel hierarchical proactive caching approach that considers both users’ future demands and AV user mobility. The proposed approach uses the non-negative matrix factorization (NMF) technique to predict user's preferences which are then used to predict users’ future demands by considering the historical popularity of videos. A user mobility prediction model is used to predict the AV users’ next location based on the current location and the planned route information which can be retrieved from the self-driving system. Based on the predicted users’ future demands and locations of AVs, the proposed caching approach can proactively cache videos that are likely to get requested at the next base station (BS) or roadside unit (RSU) that the users are moving to. The proposed approach has been evaluated under two scenarios: a highway scenario and a grid street scenario. Results show that the proposed approach can significantly improve the efficiency of caching in terms of hit ratio and the average number of hops.

Reduced value of time for autonomous vehicle users: Myth or reality?

We provide a framework of alternative underlying explanations and hypotheses related to the value of travel time changes caused by the widespread adoption of autonomous vehicles (AVs). This framework supports a scenario-based approach for infrastructure planning based on various assumptions about the value of AV travel time. The main focus of this paper is on how the value of time may remain unchanged or even increase with the advent of AVs. This is in sharp contrast to existing views about how meaningful positive utility will be associated with traveling in AVs. In future, as field data reveals behavioral changes related to AV adoption, the most appropriate hypothesis within the framework will be validated and refined for future planning and system management. The framework proposed in this paper is supported by justifications from existing and emerging theoretical concepts and hypotheses, as well as a meta-analysis extracted from contemporary but isolated reports in the literature about the possibility that AV use will increase the value of travel time.