Perception of autonomous vehicles by the modern society: a survey

Thomas, Elena; McCrudden, Connie; Wharton, Zachary; Behera, Ardhendu

doi:10.1049/iet-its.2019.0703

Cited by 36 publications

(30 citation statements)

References 22 publications

(70 reference statements)

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“…PR refers to the risk of riding an autonomous bus as perceived by an individual. Based on survey studies, although people generally expressed positive attitudes towards the autonomous driving technology, they were concerned about the risks associated with it [10,12,32,33]. According to Roche-Cerasi [12], about 52.5% of the respondents were worried about the safety of riding autonomous buses.…”

Section: Proposed Models and Hypothesesmentioning

confidence: 99%

Analysing public acceptance of autonomous buses based on an extended TAM model

Zhou

et al. 2021

IET Intelligent Trans Sys

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Integrating advantages of automated driving and mass transport, autonomous buses have broad development prospects. The research on the public acceptance of autonomous buses will help to provide a basis for formulating policies and measures, and thus have important theoretical and practical significance. In this paper, an initial theoretical model of acceptance of autonomous buses was established. An on-line survey was conducted with a total of 401 valid samples collected. The reliability and validity tests of the data showed that the internal consistency of the questionnaire was high, and the reliability and validity all met the requirements. Based on the results of structural equation modelling, the proposed model was validated and all the hypotheses were confirmed. Attitude would positively affect behavioural intention, and trust and perceived usefulness would positively affect attitudes. Moreover, both perceived ease of use and perceived comfort would have positive effects on perceived usefulness as well as on trust. Perceived risk would be negatively associated with trust. Multi-group analyses were conducted to test the moderating effects of the individual's education and knowledge of autonomous buses, and the two factors showed significant effects. Based on the results, corresponding measures can be taken to increase the acceptance of autonomous buses.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Proposed Models and Hypothesesmentioning

confidence: 99%

Analysing public acceptance of autonomous buses based on an extended TAM model

Zhou

et al. 2021

IET Intelligent Trans Sys

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“…Autonomous Vehicles (AV) will only become a success if people accept them. However, as Thomas et al concluded in [1], some persons still do not believe in Av's performance. In fact, behind the AV development still exists several technical challenges such as software complexity, real-time data analytics, testing, and verification [2].…”

Section: Introductionmentioning

confidence: 94%

“…By default, the development of some object detection models considers only the use of RGB cameras. In that models, the *This work was supported by the FUI STAR project 1 Authors are with Univ. Grenoble Alpes, INRIA Grenoble Rhône-Alpes, Chroma Team, 38334 Montbonnot, France.…”

Section: Introductionmentioning

confidence: 99%

Dynamic and Static Object Detection Considering Fusion Regions and Point-wise Features

Gómez¹,

Genevois²,

Lussereau³

et al. 2021

Preprint

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Object detection is a critical problem for the safe interaction between autonomous vehicles and road users. Deeplearning methodologies allowed the development of object detection approaches with better performance. However, there is still the challenge to obtain more characteristics from the objects detected in real-time. The main reason is that more information from the environment's objects can improve the autonomous vehicle capacity to face different urban situations. This paper proposes a new approach to detect static and dynamic objects in front of an autonomous vehicle. Our approach can also get other characteristics from the objects detected, like their position, velocity, and heading. We develop our proposal fusing results of the environment's interpretations achieved of YoloV3 and a Bayesian filter. To demonstrate our proposal's performance, we asses it through a benchmark dataset and real-world data obtained from an autonomous platform. We compared the results achieved with another approach.

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“…CAVs should interact with all the elements that make up the ecosystem, including VRUs [4]. However, beyond the technical challenges related to automate driving, the success or failure of this type of CAVs is closely related to their acceptance and social integration within the vehicle traffic ecosystem.…”

Section: Introductionmentioning

confidence: 99%

Vulnerable Road Users and Connected Autonomous Vehicles Interaction: A Survey

Reyes-Muñoz

Ibáñez

2022

Sensors

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There is a group of users within the vehicular traffic ecosystem known as Vulnerable Road Users (VRUs). VRUs include pedestrians, cyclists, motorcyclists, among others. On the other hand, connected autonomous vehicles (CAVs) are a set of technologies that combines, on the one hand, communication technologies to stay always ubiquitous connected, and on the other hand, automated technologies to assist or replace the human driver during the driving process. Autonomous vehicles are being visualized as a viable alternative to solve road accidents providing a general safe environment for all the users on the road specifically to the most vulnerable. One of the problems facing autonomous vehicles is to generate mechanisms that facilitate their integration not only within the mobility environment, but also into the road society in a safe and efficient way. In this paper, we analyze and discuss how this integration can take place, reviewing the work that has been developed in recent years in each of the stages of the vehicle-human interaction, analyzing the challenges of vulnerable users and proposing solutions that contribute to solving these challenges.

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Perception of autonomous vehicles by the modern society: a survey

Cited by 36 publications

References 22 publications

Analysing public acceptance of autonomous buses based on an extended TAM model

Analysing public acceptance of autonomous buses based on an extended TAM model

Dynamic and Static Object Detection Considering Fusion Regions and Point-wise Features

Vulnerable Road Users and Connected Autonomous Vehicles Interaction: A Survey

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