Drivers’ Evaluation of Different Automated Driving Styles: Is It Both Comfortable and Natural?

Peng, Chen; Merat, Natasha; Romano, Richard; Hajiseyedjavadi, Foroogh; Paschalidis, Evangelos; Wei, Chongfeng; Radhakrishnan, Vishnu; Solernou, Albert; Forster, Deborah; Boer, Erwin R.

doi:10.1177/00187208221113448

Cited by 14 publications

(26 citation statements)

References 38 publications

(59 reference statements)

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“…Driving style is also about how the vehicle manoeuvre is influenced by road surface and geometry, such as how it negotiates different road curvatures, or whether the ride is smooth or jerky. A number of studies have investigated the link between changes in these aspects of a driving style, and user comfort in AVs (Dettmann et al, 2021;Elbanhawi et al, 2015;Hajiseyedjavadi et al, 2022;Hartwich et al, 2018;Peng et al, 2022;Summala, 2007). For example, Bellem et al (2018) propose a range of kinematics to assist with user comfort during different manoeuvres of highly automated vehicles, such as minimising acceleration and jerk -i.e.…”

Section: The Link Between Driving Styles and Comfortmentioning

confidence: 99%

“…the rate at which the acceleration changes with respect to time. Peng et al (2022) measured user comfort for two human-like and one machine-like driving style, and found that the replay of real human participants' driving (categorised as a "defensive" driving style -driving at lower speeds), was evaluated as more comfortable than the other two.…”

Section: The Link Between Driving Styles and Comfortmentioning

confidence: 99%

“…In these situations, any unexpected or unpredictable manoeuvres of the AV (e.g., a sudden brake) may not only interrupt the user's engagement in the NDRA, but also cause concern, discomfort, or even motion sickness (Beggiato et al, 2020;Carsten & Martens, 2018;Hartwich et al, 2018;Kuiper et al, 2020). Elbanhawi et al (2015) argue that a comfortable AV ride demands natural and familiar manoeuvres (see also Peng et al, 2022), smooth control, safe operations, and the mitigation of motion sickness, in addition to the traditional (physical) factors that enhance comfort (e.g., temperature, noise, and seat design; De Looze et al, 2003;Silva, 2002). Therefore, for our original framework, we focused particularly on understanding what psychological aspects affect comfort in AVs, to help enhance users' psychological experience (i.e., how they feel about different driving styles).…”

Section: The Link Between Driving Styles and Comfortmentioning

confidence: 99%

“…Although a number of studies have described trust in automation, perhaps the most cited is one provided by Lee and See (2004) as: "the attitude that an agent will help achieve an individual's goal in a situation characterised by uncertainty and vulnerability" (p.51). Finally, Peng et al (2022) describe natural driving as "a driving style that is closest to your own" (p.6), while Hajiseyedjavadi et al (2022) use a combined description of feeling "safe/natural/comfortable" to evaluate an overall pleasant experience with automated driving. Overall, similar (positive) affects are used to describe these concepts and also comfort, when discussing the effect of automated driving style on user experience.…”

Section: The Link Between Driving Styles and Comfortmentioning

confidence: 99%

“…Comfort, as a positive user experience of automated driving, is essential for the broad acceptance of Automated Vehicles (AVs), (Dichabeng et al, 2021;Nordhoff, Malmsten, et al, 2021;Paddeu et al, 2020;Siebert et al, 2013). When being driven by a higher-level AV (SAE Level 4+, SAE International, 2021), automated driving styles, such as the vehicle's kinematic behaviour, the distance it keeps with other, roadbased, objects, and how it negotiates different road geometries, play an important role in user comfort (Beggiato et al, 2020;Bellem et al, 2016;Diels & Bos, 2015;Peng et al, 2022). Here, a wide range of factors are considered relevant to user comfort when driven by AVs.…”

Section: Introductionmentioning

confidence: 99%

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Conceptualising user comfort in automated driving: Findings from an expert group workshop

Peng¹,

Horn²,

Madigan³

et al. 2023

Preprint

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Automated driving needs to be comfortable to encourage the broad acceptance and usage of automated vehicles (AVs). However, current research provides limited knowledge on the descriptions and influencing factors of user comfort in automated driving, especially from the perspective of an AV’s driving styles. This paper presents results from an online workshop, in which nine experts with hands-on experience of AVs, and a long track record of research in this context, brainstormed and discussed a series of topics related to user comfort in automated driving. Results showed that a wide range of terms were used to describe user comfort and discomfort, when the attendees considered being driven by either currently available transport modes (e.g., taxi/bus/train) or higher-level AVs (with neither requiring control of the vehicle by the user). All of the terms used for existing vehicles were found to apply to AVs, but additional aspects of comfort/discomfort were revealed for AVs. This suggests that what makes existing transport modes comfortable is not the same as that for higher-level AVs. Regarding the relationship between comfort and discomfort in automated driving, seven and ten categories of terms were identified for comfort and discomfort, respectively, with both the number and the nature of these categories differing across the two states. This suggests that, to ensure user comfort in automated driving, additional factors that affect comfort must be considered, rather than solely mitigating discomfort. By integrating these multiple aspects that affect user comfort, knowledge from the workshop was used to develop a conceptual framework, to explain how AV driving styles, as well as other, non-driving-related factors, affect user comfort, from both a physical and psychological perspective. This work provides an overview of experts’ insights on the factors that affect comfort in automated driving, and will facilitate a more comprehensive definition, and more accurate measurement, of user comfort, facilitating the design of more comfortable and acceptable automated driving for future vehicles.

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