Passengers’ acceptance and perceptions of risk while riding in an automated vehicle on open, public roads

Pascale, Michael; Rodwell, David; Coughlan, Pete; Kaye, Sherrie-Anne; Demmel, Sébastien; Dehkordi, Sepehr Ghasemi; Bond, Andy; Lewis, Ioni; Rakotonirainy, Andry; Glaser, Sébastien

doi:10.1016/j.trf.2021.10.012

Cited by 11 publications

(7 citation statements)

References 40 publications

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“…It has been shown that perceived risk is negatively related to behavioral attitudes. It has been shown that functional perceived risk is more likely to influence user attitudes toward new products than affective perceived risk (Pascale et al 2021). However, this study inferred that user perceptions of maneuvering risks in humanmachine collaborative driving of AVs may negatively affect their perceived behavioral control toward adopting AVs.…”

Section: Construction Of the Improved Modelmentioning

confidence: 99%

The Role of Technical Safety Riskiness and Behavioral Interventions in the Public Acceptance of Autonomous Vehicles in China

Deng

Guo

Yu-xi

et al. 2023

J. Transp. Eng., Part A: Systems

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After reports of self-driving car accidents, public acceptance of autonomous vehicles (AVs) has shown a downward trend. To explore factors influencing this decline of acceptance and to bring acceptance back to the normal range, this study proposed a new factor, human-manipulated risk perception (HMRP), in the context of human-machine collaborative driving. On this basis, we explored the relationship between HMRP and acceptance of AVs and the mechanism of influence. Furthermore, we proposed a behavioral intervention approach to mediate and moderate the relationship between the two. First, an improved acceptance model was developed based on HMRP. Subsequently, a survey was executed, and 300 valid questionnaires were returned. Test results for the improved model showed that HMRP had a significant negative effect on acceptance of AVs. A hybrid model of mediation and moderation was designed, and user experience was introduced as a moderating variable. The results showed that the proposed behavioral intervention strategy was effective in attenuating the negative effect of HMRP on acceptance of AVs. This study provides theoretical support and methodological guidance for developing behavioral interventions to improve user acceptance.

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Section: Construction Of the Improved Modelmentioning

confidence: 99%

The Role of Technical Safety Riskiness and Behavioral Interventions in the Public Acceptance of Autonomous Vehicles in China

Deng

Guo

Yu-xi

et al. 2023

J. Transp. Eng., Part A: Systems

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show abstract

“…Honda) and ADT companies (e.g. Waymo) are gradually penetrating the commercial market [7] and being deployed on the public roads [8], respectively.…”

Section: Introductionmentioning

confidence: 99%

Examining the feasibility of current spiral curve design controls for LiDAR‐based automated vehicles

Wang

Mao

et al. 2022

IET Intelligent Trans Sys

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Enabling large-scale deployment of automated vehicles (AVs) in the near future requires answering the question firstly: whether AVs could safely adapt to as-built road geometry? This study aims to examine the feasibility of current spiral curve design controls for LiDAR-based AVs (LAVs) from the perspective of the available sight distance (ASD). A series of tests featuring the design speed (V d ), lengths of tangent (L T ) and spiral (L S ), circular curve radii (R), and point thresholds for detection (N T ) were simulated in PreScan/MATLAB/Simulink co-simulation platform. The ASDs affected by those parameters' combined effects were analyzed and compared with required stopping sight distances (RSDs) of human-driven vehicles (HVs) and level 3 to 5 (L3-L5) LAVs followed by proposing the ASD-oriented safe speeds and the corresponding speed limits. The results indicate that: (1) the combination of the tangent-spiral curve-circular curve causes a shorter ASD than that without the spiral curve; (2) a longer spiral curve causes a shorter ASD; and (3) only a low-type combination of R, L S , V d conditions is feasible for L3 LAVs while L4 or L5 LAVs have difficulties in dealing with high-type conditions. These findings help understand the ASD for AVs and provide safety-critical speed references for administrators. INTRODUCTIONAutomated vehicles (AVs), generally referring to vehicles equipped with level 3 to 5 automation systems [1], are anticipated to serve as an optimal solution for improving traffic safety mainly by mitigating driver errors [2-4]. For example, AVs are capable of obtaining more stable perception-functional performances and response ability through their sensor-based perception systems and onboard computing units [5], while the perception system of human drivers relying on the eyes and brains is easily affected by their psychophysical conditionsThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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“…In our study, the descriptive increase in behavioral intention was not significant, so the results do not support this hypothesis. This contrasts with studies by Rahman et al [21] and Pascale et al [22]. However, other work also reports no significant increases in acceptance [15].…”

Section: Summary Of the Resultsmentioning

confidence: 63%

“…A study of advanced driver assistance systems (ADAS) in a driving simulator showed that there was a higher behavioral intention in the group that could try the ADAS in the simulator than in the group that could only read a description of it [21]. In a real-life traffic driving study by Pascale et al [22], acceptance of an automated car at SAE level 3 was found to increase after driving on an open road compared to the acceptance before driving.…”

Section: Introductionmentioning

confidence: 99%

Acceptance of Automated Shuttles—Application and Extension of the UTAUT-2 Model to Wizard-of-Oz Automated Driving in Real-Life Traffic

Rybizki

Ihme

Nguyen

et al. 2022

Future Transportation

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Automated shuttles can make public transport more attractive and sustainable. Still, their successful implementation requires a high level of acceptance among users. This study investigates the impact of the predictors performance expectancy, social influence, facilitating conditions, hedonic motivation, and perceived risk of The Unified Theory of Acceptance (UTAUT)-2 on the behavioral intention to use automated shuttles. In earlier work, UTAUT-2 has already been successfully applied to study the acceptance of autonomous public transport. Here, we employed the UTAUT-2 to assess acceptance of a Wizard-of-Oz automated shuttle in real-life traffic, in a study with 35 participants, before and after a first ride and after a second ride on which two incidents occurred. The results show that behavioral intention to use automated shuttles is high even before the first ride and remains high after experiencing automated driving. Performance expectancy was the only significant predictor of behavioral intention for all measurement time points. The explanatory power of the model almost doubles from pre-ride to post-ride. The results indicate a crucial role of performance expectancy for the acceptance of automated shuttles at the current stage of implementation and provide guidance for a successful development and implementation of autonomous public transport.

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Passengers’ acceptance and perceptions of risk while riding in an automated vehicle on open, public roads

Cited by 11 publications

References 40 publications

The Role of Technical Safety Riskiness and Behavioral Interventions in the Public Acceptance of Autonomous Vehicles in China

The Role of Technical Safety Riskiness and Behavioral Interventions in the Public Acceptance of Autonomous Vehicles in China

Examining the feasibility of current spiral curve design controls for LiDAR‐based automated vehicles

Acceptance of Automated Shuttles—Application and Extension of the UTAUT-2 Model to Wizard-of-Oz Automated Driving in Real-Life Traffic

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