How Do You Want to be Driven? Investigation of Different Highly-Automated Driving Styles on a Highway Scenario

Roßner, Patrick; Bullinger, Angelika C.

doi:10.1007/978-3-030-20503-4_4

Cited by 15 publications

(11 citation statements)

References 9 publications

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“…Results also suggest that different automated driving styles are sometimes found to be preferred by different groups of users, when evaluated in terms of comfort, safety and pleasantness, although findings are inconclusive. For instance, a more defensive driving style, with slower lane changing features, and lower acceleration, was favoured by most participants, when compared with a higheracceleration, more assertive, driving style (Rossner & Bullinger, 2020). In addition, Hartwich et al (2018) report that familiar driving styles (a replay of participants' own driving) were more favoured by younger drivers (25-35 years), while faster and unfamiliar automated driving styles (that of the younger drivers') were preferred by a group of older drivers (65-84 years) in the same study, leading Hartwich et al (2018) to suggest that purely mimicking drivers' personal manual driving habits may not be suitable in this context, as this approach is not suitable for all age groups.…”

Section: Introductionmentioning

confidence: 94%

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

Peng¹,

Merat²,

Romano³

et al. 2021

Preprint

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Objective: This study investigated users’ subjective evaluation of three highly automated driving styles, in terms of comfort and naturalness, when negotiating a UK road in a high-fidelity, motion-based, driving simulator. Background: Comfort and naturalness are thought to play an important role in contributing to users’ acceptance and trust of automated vehicles (AVs), although not much is understood about the types of driving style which are considered comfortable or natural. Method: A driving simulator study, simulating roads with different road geometries and speed limits, was conducted. Twenty-four participants experienced three highly automated driving styles, two of which were recordings from human drivers, and the other was based on a machine learning (ML) algorithm, termed Defensive, Aggressive, and Turner respectively. Participants evaluated comfort or naturalness of each driving style, for each road segment, and completed a Sensation Seeking (SS) questionnaire, which assessed their risk-taking propensity. Results: Participants regarded human-like driving styles as more comfortable and natural, compared with the less human-like, ML-based, driving controller. However, between the two human-like controllers, only the Defensive style was considered comfortable, especially for the more challenging road environments. Differences in preference for controller by driver trait were also observed, with the Aggressive driving style evaluated as more natural by the high sensation seekers. Conclusion: Participants were able to distinguish between human- and machine-like AV controllers. A range of psychological concepts must be considered for the subjective evaluation of controllers. Application: Knowing how different driver groups evaluate automated vehicle controllers is important to design more acceptable systems in the future.

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Section: Introductionmentioning

confidence: 94%

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

Peng¹,

Merat²,

Romano³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…A common approach to describe a driving style is to use objective parameters such as vehicle kinematics and environment context. In the past, a variety of parameter representations have been introduced, such as time series data [3,42], statistical characteristics [7,43,44], or latent variables, representing a dimensionless combination of different parameter types [9,45,46]. In order to differentiate between various driving styles, classification techniques such as manual or data-driven methods are commonly applied.…”

Section: Driving Stylementioning

confidence: 99%

“…Preferences for automated driving have been analyzed for different maneuvers such as lane changes [8,10,11,36,43], car-following [56][57][58][59], decelerating to a slower lead vehicle [10,36], or across different maneuvers and scenarios [6,7,25,26]. A common approach by previous studies in this context is to let users experience a predefined driving style in comparison with other driving styles or their own prerecorded driving style, using driving simulators.…”

Section: Driving Stylementioning

confidence: 99%

Influences of Personal Driving Styles and Experienced System Characteristics on Driving Style Preferences in Automated Driving

Vasile,

Seitz,

Staab

et al. 2023

Applied Sciences

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As automated driving technology continues to advance, the question of how users prefer to be driven in their new, more passive role is becoming increasingly relevant. In this paper, a real-world study on a German motorway with 42 participants was conducted to analyze driving style preferences for conditional automated driving, taking the participants’ personal driving style into account. In the first part, participants’ personal driving style (PDS) was recorded during a manual drive in the first half on a given route. For the second half, participants were asked to demonstrate their desired driving style (DDS) for conditional automated driving. In the second part, participants were driven on the same route in a defensive automated vehicle (AV) while rating driving comfort and safety. Subsequently, the relationship between driving style differences and ratings was analyzed. Furthermore, a comparison between PDS and DDS was performed. The results show that very defensive to moderate drivers perceived the AV’s driving style, being similar to their own, as equally safe but significantly more comfortable than moderate to very aggressive drivers. No influence of driving style differences was found on the increase in trust. However, a significant increase in trust after experiencing an automated vehicle has been observed. Furthermore, the rated system characteristics of anthropomorphism, safety, and overall driving strategy had a significant influence on driving style preferences for AVs. This study makes an important contribution to answering the question of how users want to be driven in conditional automated driving.

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“…This finding aligns with the hypothesis -i.e. regardless of DS type, drivers would prefer to be in a more defensive DS of AVs [10], [17], [47], [48].…”

Section: Test Ride Rating Analysis 3221 Driving Style Ratingmentioning

confidence: 99%

Analysis of User’s Comfort on Automated Vehicle Riding Simulation using Subjective and Objective Measurements

Norzam

Karjanto

Yusof

et al. 2022

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The naturalistic study investigated the potential influence of personal driving preferences (assertive and defensive driving style) on users; comfort when being driven in an automated vehicle with a defensive driving style. Adopted the Wizard of Oz design, the study involved three phases: pre-, during, and post-driven to measure their comfort, perceived safety, and likeness as well as motion sickness propensity through self-report questionnaire and heart rate variation. After answering a set of questionnaires, participants were exposed to simulated driving in an automated vehicle with a defensive driving style. A statistical analysis produced no statistically significant difference between assertive and defensive participants. This indicates an overall preference, perceived comfort without severe motion sickness propensity to the defensive driving style of the autonomous vehicle, regardless of participants’ personal driving styles.

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How Do You Want to be Driven? Investigation of Different Highly-Automated Driving Styles on a Highway Scenario

Cited by 15 publications

References 9 publications

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

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

Influences of Personal Driving Styles and Experienced System Characteristics on Driving Style Preferences in Automated Driving

Analysis of User’s Comfort on Automated Vehicle Riding Simulation using Subjective and Objective Measurements

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