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

Xu, Zhigang; Zhang, Kaifan; Min, Haigen; Wang, Zhen; Zhao, Xiangmo; Liu, Peng

doi:10.1016/j.trc.2018.07.024

Cited by 421 publications

(298 citation statements)

References 56 publications

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“…In , performance expectancy and effort expectancy measuring the component 'shuttle effectiveness' , which pertained to the performance of L4 ADS-DVs in comparison with respondents' existing travel, significantly correlated with the component 'intention to use' , which corresponds with the UTAUT construct 'behavioral intention' . The effect of facilitating conditions (or its equivalent 'perceived behavioral control' 5 , 'helpfulness' , 'technical support' , '(technical) self-efficacy' , 'conceptual compatibility/fit' , 'lifestyle fit' and 'technology confidence') on the intentions to use ADS-DVs was supported in the study of Buckley, Kaye, and Pradhan (2018) 2016 ;Salonen 2018;Nazari et al 2018;Xu et al 2018). Topics around safety and security include equipement and system failure, cyber security or the fear of terrorism and hacking, and system performance in poor weather and terrain and unexpected conditions (e.g., Bansal & Kockelman 2017;Dong et al 2017;Gurumurthy 2017;Penmetsa et al 2019;Tussyadiah et al 2017).…”

Section: Exposure To Avs: Knowledge and Experiencementioning

confidence: 94%

“…While academics and practitioners do not seem to expect AVs to be error-free (Kalra and Paddock 2016), the general public does not seem to be willing to accept fatalities that arise due to automation as exemplified by the public reactions to the accidents with the partly-automated vehicles from Tesla and Uber (Eden et al 2017). Xu et al (2018) found that perceived safety was significantly correlated with the intention to use and willingness to re-ride in AVs, which aligns with Nazari, Noruzoliaee, and Mohammadian (2018) who revealed that safety concerns reduced interest in shared automated vehicles.…”

Section: Exposure To Avs: Knowledge and Experiencementioning

confidence: 99%

“…Bansal and Kockelman (2018) corroborated the effect of social influence on the willingness to adopt AVs by showing that some individuals were less likely to rely on the adoption rates of their friends (i.e., disabled, bachelor's degree holders and familiar with car-sharing) than others (i.e., elderly, single and Caucasian ethnicity). Acheampong and Cugurullo (2019) found a positive relationship between subjective norm (equivalent to social influence) and the perceived benefits (equivalent to performance expectancy, see Xu et al, 2018;Zhang et al, 2019), and ease of use of automated driving technology (equivalent to effort expectancy), and a positive relationship between subjective norm and perceived behavioral control (equivalent to facilitating conditions). Hartwich, Beggiato, and Krems (2018) postulate that affective variables are of growing importance for drivers' vehicle choices, and found a significant effect of enjoyment on automated driving.…”

Section: Symbolic-affective Aspects Of Ava: Hedonic Motivation and Somentioning

confidence: 99%

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A multi-level model on automated vehicle acceptance (MAVA): a review-based study

Nordhoff

Kyriakidis

Arem

et al. 2019

Theoretical Issues in Ergonomics Science

153

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Automated vehicle acceptance (AVA) is a necessary condition for the realisation of higher-level objectives such as improvements in road safety, reductions in traffic congestion and environmental pollution. On the basis of a systematic literature review of 124 empirical studies, the present study proposes MAVA, a multi-level model to predict AVA. It incorporates a process-oriented view on AVA, considering acceptance as the result of a four-stage decision-making process that ranges from the exposure of the individual to automated vehicles (AVs) in Stage 1, the formation of favourable or unfavourable attitudes towards AVs in Stage 2, making the decision to adopt or reject AVs in Stage 3, to the implementation of AVs into practice in Stage 4. MAVA incorporates 28 acceptance factors that represent seven main acceptance classes. The acceptance factors are located at two levels, i.e., micro and meso. Factors at the micro-level constitute individual difference factors (i.e., sociodemographics, personality and travel behaviour). The meso-level captures the exposure of individuals to AVs, instrumental domain-specific, symbolic-affective and moral-normative factors of AVA. The literature review revealed that 6% of the studies investigated the exposure of individuals to AVs (i.e., knowledge and experience). 22% of the studies investigated domain-specific factors (i.e., performance and effort expectancy, safety, facilitating conditions, and service and vehicle characteristics), 4% symbolic-affective factors (i.e., hedonic motivation and social influence), and 12% moral-normative factors (i.e., perceived benefits and risks). Factors related to a person's socio-demographic profile, travel behaviour and personality were investigated by 28%, 15% and 14% of the studies, respectively. We recommend that future studies empirically verify MAVA using longitudinal or experimental studies.

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Section: Exposure To Avs: Knowledge and Experiencementioning

confidence: 94%

Section: Exposure To Avs: Knowledge and Experiencementioning

confidence: 99%

Section: Symbolic-affective Aspects Of Ava: Hedonic Motivation and Somentioning

confidence: 99%

See 1 more Smart Citation

A multi-level model on automated vehicle acceptance (MAVA): a review-based study

Nordhoff

Kyriakidis

Arem

et al. 2019

Theoretical Issues in Ergonomics Science

153

View full text Add to dashboard Cite

show abstract

“…New technologies such as autonomous vehicle and shared mobility could be applied for the specific traffic situations in the fringe area to improve the traffic operations. 20,21 Furthermore, old town fringe area is updated continuously with the boundaries growing or shrinking. Thus, a dynamic view on the evolution process of the old town fringe could help better understand the renewal mechanism of such area, which could lead to more interesting findings.…”

Section: Suggestions and Conclusionmentioning

confidence: 99%

Old town fringe recognition and travel characteristics analysis based on multi-source data fusion

Zhou

et al. 2019

Advances in Mechanical Engineering

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Old town fringe area refers to a surrounding zone of an old town. The fringe undertakes the important function of evacuating traffic volume between suburb districts and central city. Traffic flow and travel behavior of the old town fringe are complex and different from other areas. Thus, recognition of fringe area is of importance for researchers to better understand the unique travel features and propose proper policies for fringe renewal. This study took the city of Nanjing as an example and fused the residents' travel survey data and the point-of-interest data for fringe recognition. The study estimated the travel intensity of each travel analysis zone per day. The mutation point of travel intensity was decided to divide the fringe. The point-of-interest data was used for validating the boundaries of the fringe. The fusion of the two data sets jointly decided the core old town fringe area. The travel behavior characteristics of the fringe area, including the fringe internal trips, crossing fringe trips, and those with only origin or destination in the fringe, were then evaluated and policy suggestions were provided. The findings of the study will benefit the urban space planning and coordinated transportation system development in the fringe area of old towns.

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“…This causes serious road safety and traffic congestion problems, and also leads to huge fuel consumption and environmental pollution issues. Based on the concept of cooperative automated driving [1], grouping vehicles on the road into platoons is one possible technical solution to these problems. Fig.…”

Section: Introductionmentioning

confidence: 99%

Development and verification of cooperative adaptive cruise control via LTE‐V

Wang

Chen

Wang

et al. 2019

IET Intelligent Transport Systems

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In this paper, we present a testbed platform for realizing cooperative adaptive cruise control (CACC) enabled by LTE-V (LTE-vehicle). The platform is developed on a platoon of vehicles, each of which is equipped with a suite of on-board sensing and computing devices for environment perception and automated vehicle control, as well as an LTE-V transceiver for high-performance vehicle-to-vehicle (V2V) communication. The hardware architecture and software architecture, especially the perception and control methods, of the platform are described. Field experiments in different road conditions are conducted to verify the feasibility of our platform. The results also show the potential of V2V communications via LTE-V in terms of improving the sensing capability of individual vehicle's on-board sensors.2 information (e.g., speed, wheel angle, heading and location, throttle and brake actions, etc.) to achieve better environment perception, has attracted wide research attentions. The dedicated short-range communications (DSRC) technology has been considered as the solution to realizing V2X communication for years. The US Federal Communication Commission (FCC) has allocated a 75 MHz spectrum in the 5.9 GHz band for DSRC [25]. The information exchanged among vehicles can be encoded into Basic Safety Message (BSM) and periodically broadcasted by vehicles according to the Society of Automotive Engineering (SAE) standard [26]. However, due to the ad hoc working nature, DSRC may not be able to fully guarantee highly-reliable and low-delay communication in all traffic conditions.In the past few years, the cellular-based LTE-V (LTE vehicle) technology [27] has started to be investigated in many countries and research communities. For example, a data exchange standard [28] is currently being developed by the Society of Automotive Engineers of China (SAE-China). Cooperative automated driving is seen as one of the most important use cases supported by LTE-V and LTE-eV2X (the next generation of LTE-V) according to 3GPP TR 22.886 [29]. LTE-V supports both V2V (vehicle-to-vehicle) communication over the peer-to-peer PC5 interface, and V2N (vehicle-to-network) communication over the LTE-Uu interface. Therefore, the interference management and scheduling of V2V communication traffic can be assisted by base stations to obtain a more reliable and efficient message dissemination compared with DSRC, especially in the highly mobile environment. The transmission delay of LTE-V is expected to be about 25ms through V2V direct communication, which meets the latency requirements of platooning defined in 3GPP TR 22.886. A comparative analysis between LTE-V and DSRC [30] is shown in Table 1, which clearly demonstrates the advantages of the former technology.

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

Cited by 421 publications

References 56 publications

A multi-level model on automated vehicle acceptance (MAVA): a review-based study

A multi-level model on automated vehicle acceptance (MAVA): a review-based study

Old town fringe recognition and travel characteristics analysis based on multi-source data fusion

Development and verification of cooperative adaptive cruise control via LTE‐V

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