Exploring automated vehicle driving styles as a source of trust information

Ekman, Fredrick; Johansson, Mikael; Bligård, Lars-Ola; Karlsson, MariAnne

doi:10.1016/j.trf.2019.07.026

Cited by 68 publications

(36 citation statements)

References 37 publications

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“…There have been several cases of research into investigating driving styles as a source of trust information (e.g. [15]); however, there is no previous study exploring the effects of a personalised AV approach, which adapts to a driver's driving style, on drivers' trust. This study presents a new approach of a personalised AV, which is able to identify user driving behaviours and thereby adapt its driving style accordingly.…”

Section: Driving Stylementioning

confidence: 99%

“…Driving style refers to how the driving process is conducted [13] and depends on a driver's individual driving habits (such as driving speed). Inevitably, different drivers have different driving styles [15], and studies into driving styles in AVs have only begun recently [30]. A small number of these studies have indicated that the driving style of an AV could be important for users' perceptions and understanding of the AV .…”

Section: Introductionmentioning

confidence: 99%

“…Hence, users require tailored solutions to explain what can be improved in the driving style and how this may be achieved [40]. Ekman et al [15] recently emphasised the importance of driving style for user acceptance in the field of autonomous driving.…”

Section: Introductionmentioning

confidence: 99%

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Exploring Personalised Autonomous Vehicles to Influence User Trust

Sun

Tang

et al. 2020

Cogn Comput

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Trust is a major determinant of acceptance of an autonomous vehicle (AV), and a lack of appropriate trust could prevent drivers and society in general from taking advantage of such technology. This paper makes a new attempt to explore the effects of personalised AVs as a novel approach to the cognitive underpinnings of drivers' trust in AVs. The personalised AV system is able to identify the driving behaviours of users and thus adapt the driving style of the AV accordingly. A prototype of a personalised AV was designed and evaluated in a lab-based experimental study of 36 human drivers, which investigated the impact of the personalised AV on user trust when compared with manual human driving and non-personalised AVs. The findings show that a personalised AV appears to be significantly more reliable through accepting and understanding each driver's behaviour, which could thereby increase a user's willingness to trust the system. Furthermore, a personalised AV brings a sense of familiarity by making the system more recognisable and easier for users to estimate the quality of the automated system. Personalisation parameters were also explored and discussed to support the design of AV systems to be more socially acceptable and trustworthy.

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Section: Driving Stylementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Exploring Personalised Autonomous Vehicles to Influence User Trust

Sun

Tang

et al. 2020

Cogn Comput

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“…However, there are currently very few studies, and limited available knowledge, on how changes to the driving style of automated vehicles is perceived by users, and how/if this affects their ride experience, and ultimate acceptance, of these vehicles. Studies in manual driving suggest that drivers have a range of different driving styles, from calm or defensive, to aggressive or assertive (Eboli, Mazzulla, & Pungillo, 2017;Ekman, Johansson, Bligård, Karlsson, & Strömberg, 2019;Murphey, Milton, & Kiliaris, 2009;Yusof et al, 2016). Here, driving style can simply refer to how drivers choose to drive a vehicle, or the driving habits that they obtain with more driving experience (Elander, West, & French, 1993).…”

Section: Introductionmentioning

confidence: 99%

Effect of Environmental Factors and Individual Differences on Subjective Evaluation of Human-like and Conventional Automated Vehicle Controllers

Hajiseyedjavadi¹,

Boer²,

Romano³

et al. 2021

Preprint

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Achieving optimal performance in human-machine systems, such as highly automated vehicles, relies, in part, on individuals’ acceptance and use of the system, which is in turn affected by their enjoyment of engaging with, or experiencing, the system. This driving simulator study investigated individuals’ real-time subjective evaluation of four different Automated Vehicle (AV) driving styles, in different environmental contexts. Twenty-four participants were recruited to manually drive a contextually rich simulator environment, and to experience human-like and non-human-like AV driving styles, as well as the automated replay of their own manual drive. Their subjective real-time feedback towards these driving styles was analyzed. Our results showed that participants gave higher positive feedback towards the replay of their own drive, compared to the other three controllers. This difference was statistically significant, when compared to the high-speed controller (named as Fast), particularly for sharp curves. With respect to the replay of their own drive, participants gave higher negative feedback when navigating an Urban environment, compared to Rural settings. Moreover, changes in roadside furniture affected individuals’ feedback, and this effect was more prominent when the vehicle was driving closer to the edge of the road. Based on our results, we conclude that individuals’ perception of different AV driving styles changes based on different environmental conditions, including, but not limited to, road geometry and roadside furniture. These findings suggest that humans prefer a slower human-like driving style for AV controllers that adapts its speed and lateral offset to roadside objects and furniture. Investigating individual differences in AV driving style preference showed that low Sensation Seeking individuals preferred the slower human-like controller more than the faster human-like controller. Consideration of this human-centered feedback is important for the design of future AV controllers, to enhance individuals’ ride experience, and potentially improve acceptance and use of these vehicles.

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“…A possible alternative would have been to use a think-aloud protocol in a naturalistic driving situation, where the driver at each instant indicates where (s)he is looking. Think-aloud protocols have been used in real traffic research, for example to understand trust ratings in automated vehicle technology (Ekman et al, 2019 ), and to measure situation awareness (Key et al, 2016 ). In both cases, verbal utterances were based on (prospective) interactions with other road users, such as passing a pedestrian crossing or taking a roundabout.…”

Section: Discussionmentioning

confidence: 99%

Does agreement mean accuracy? Evaluating glance annotation in naturalistic driving data

2020

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Naturalistic driving studies often make use of cameras to monitor driver behavior. To analyze the resulting video images, human annotation is often adopted. These annotations then serve as the 'gold standard' to train and evaluate automated computer vision algorithms, even though it is uncertain how accurate human annotation is. In this study, we provide a first evaluation of glance direction annotation by comparing instructed, actual glance direction of truck drivers with annotated direction. Findings indicate that while for some locations high annotation accuracy is achieved, for most locations accuracy is well below 50%. Higher accuracy can be obtained by clustering these locations, but this also leads to reduced detail of the annotation, suggesting that decisions to use clustering should take into account the purpose of the annotation. The data also show that high agreement between annotators does not guarantee high accuracy. We argue that the accuracy of annotation needs to be verified experimentally more often.

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Exploring automated vehicle driving styles as a source of trust information

Cited by 68 publications

References 37 publications

Exploring Personalised Autonomous Vehicles to Influence User Trust

Exploring Personalised Autonomous Vehicles to Influence User Trust

Effect of Environmental Factors and Individual Differences on Subjective Evaluation of Human-like and Conventional Automated Vehicle Controllers

Does agreement mean accuracy? Evaluating glance annotation in naturalistic driving data

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