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

Major steps towards implementation of autonomous and connected transport are being taken nowadays. The trend of automation technology being used in vehicles by the most important vehicle manufacturing industries is expected to move closer to high or fully Autonomous Vehicles (AVs) through technological advancements in sectors of robotics and artificial intelligence. Vehicles with autonomous driving capabilities are planning to be available on market, in full scale, in the next years. In the longer term substantial benefits are mainly expected for accessibility to transport, safety, traffic flow, emissions, fuel use and comfort. All these potential societal benefits will not be achieved unless AVs are accepted and used by a critical mass of people. Addressing these challenges, this paper: (a) proposes a technology acceptance modelling process by extending the original Technology Acceptance Model (TAM) to explain and predict consumers’ intensions towards AVs, (b) based on the proposed TAM-extended framework, a 30-question survey was conducted in order to investigate the factors influencing consumers’ intensions to use and accept AVs. Results show that the constructs of perceived usefulness, perceived ease to use, perceived trust and social influence, are all useful predictors of behavioral intentions to have or use AVs, with perceived usefulness having the strongest impact. The insights derived from this study could significantly contribute to ongoing research related to technology acceptance of AVs and are expected to allow automobile industries to improve their design and technology.

An empirical investigation on consumers’ intentions towards autonomous driving

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

The inevitable need to develop new delivery practices in last-mile logistics arises from the enormously growing business to consumer (B2C) e-commerce and the associated challenges for logistics service providers. Autonomous delivery vehicles (ADVs) are believed to have the potential to revolutionise last-mile delivery in a way that is more sustainable and customer focused. However, if not widely accepted, the introduction of ADVs as a delivery option can be a substantial waste of resources. At present, the research on consumers’ receptivity of innovations in last-mile delivery, such as ADVs, is limited. This study is the first that investigates the users’ acceptance of ADVs in Germany by utilising an extended Unified Theory of Acceptance and Use of Technology (UTAUT2) and adapted it to the context of ADVs in last-mile delivery. Quantitative data was collected through an online survey approach (n = 501) and structural equation modelling was undertaken. The results indicate that price sensitivity is the strongest predictor of behavioural intention (i.e., user acceptance), followed by performance expectancy, hedonic motivation, perceived risk, social influence and facilitating conditions, whereas no effect could be found for effort expectancy. These findings have important theoretical and practical contributions in the areas of technology acceptance and last-mile delivery.

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Acceptance of autonomous delivery vehicles for last-mile delivery in Germany – Extending UTAUT2 with risk perceptions

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

The invention discloses a vehicle queue simulation system and method based on virtual reality and a driving simulator The simulation system includes a driving simulation system, a virtual reality display system, a simulation system client, and a simulation system server The driving simulation system realizes the operation of the leader vehicle in a vehicle queue in a virtual simulation environment controlled by a human driver The virtual reality display system feeds back the operation states of the vehicle queue in a visual virtual simulation environment and the virtual simulation environment and the operation state of each vehicle in the vehicle queues to the human driver and an observer of the simulation process The simulation system client provides a human-computer interaction interface of the simulation server, and the simulation system server provides a software platform for the virtual simulation environment construction and the vehicle queue simulation The system can solveproblems that a conventional simulation system and method is low in fidelity of simulation environments and is small in number of operation states of the vehicle queues, and improves the simulation reality, flexibility and observation performance of the vehicle queues

Vehicle queue simulation system and method based on virtual reality and driving simulator

The invention belongs to the technical field of intelligent automobile operation state monitoring, and discloses a method and device for automatically detecting a fault of an automobile vehicle-mounted sensor. Whether the sensor is faulty is judged by an original data verification step and a frame rate comparison step, whether an image sensor and a laser radar sensor are faulty is further judged by an image data judging step and a distance data judging step, a corresponding fault code can also be output, and a faulty sensor type and a fault level are analyzed through the fault code to prompt adriver to exit automatic driving or auxiliary driving so as to ensure the safety of passengers in the automobile; and a detection result can also be sent to a remote monitoring center in time, and the remote monitoring personnel can control the faulty automobile or notify a vehicle owner in time through a remote background so as to avoid a safety accident.

Method and device for automatically detecting fault of automobile vehicle-mounted sensor

The invention belongs to the technical field of intelligent vehicle positioning, in particular to a vehicle motion trajectory estimation method and system based on multi-line laser radar, wherein themethod comprises the following steps of: obtaining a coarse motion trajectory according to an initial posture of the vehicle and an output value of an inertial sensor at different times; getting three-dimensional point cloud data of multi-line laser radar at different time and rasterizing them to get gray image; performing feature matching on the feature points between two gray images at adjacenttime points to obtain feature matching point pairs; according to the feature matching point pairs, obtaining the transformation relationship between the point cloud data at the adjacent time is obtained. According to the transformation relation, the accurate moving track of the vehicle, and carrying out the SURF matching in a specific range based on the roughly selected track, so that the computational amount and the computational time of the SURF feature matching are reduced. The vehicle trajectory estimation method does not need to rely on high-precision map, GPS and UWB and other auxiliarypositioning technology, and has the strong environmental suitability and wide application range.

Zhang Kaifan

Papers

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

Vehicle queue simulation system and method based on virtual reality and driving simulator

Method and device for automatically detecting fault of automobile vehicle-mounted sensor

A vehicle motion trajectory estimation method and system based on multi-line laser radar