Effects of non-driving related tasks in prolonged conditional automated driving – A Wizard of Oz on-road approach in real traffic environment

Jarosch, Oliver; Paradies, Svenja; Feiner, Daniel; Bengler, Klaus

doi:10.1016/j.trf.2019.07.023

Cited by 24 publications

(8 citation statements)

References 13 publications

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“…Here, the passenger is free to do real-world tasks in the car, yet the user experience might be limited, e.g., driving speed or traffic. Wizard of Oz Vehicles [2,22,28,47,48], provide a realistic driving environment in a real car. Such approaches include the deception of the users by giving them an illusion to interact with a real system (i.e., drive in a fully automated car) while in fact the system is operated by a human operator ("wizard").…”

Section: Methods To Investigate Ndrasmentioning

confidence: 99%

A Wizard of Oz Field Study to Understand Non-Driving-Related Activities, Trust, and Acceptance of Automated Vehicles

Detjen

Pfleging

Schneegaß

2020

12th International Conference on Automotive User Interfaces and Interactive Vehicular Applications

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Section: Methods To Investigate Ndrasmentioning

confidence: 99%

A Wizard of Oz Field Study to Understand Non-Driving-Related Activities, Trust, and Acceptance of Automated Vehicles

Detjen

Pfleging

Schneegaß

2020

12th International Conference on Automotive User Interfaces and Interactive Vehicular Applications

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“…Simulated driving experiments have confirmed that longer periods of monitoring self-driving cars see drivers exhibiting slower braking and poorer steering responses during take-over, thus increasing the probability of a crash [26]. Another study has shown that supervising selfdriving cars trigger passive fatigue within 25 minutes and impeded take-over performance after 50 minutes [17]. In other words, passive fatigue results in deteriorated reaction time and impaired perception, consequently contributing to poorer take-over from automated driving.…”

Section: Passive Fatiguementioning

confidence: 93%

Self-Driving Cars and Driver Alertness

Tran¹,

Nayak²

2021

Preprint

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Recent years have seen growing interest in the development of self-driving vehicles that promise (or threaten) to replace human drivers with intelligent software. However, current self-driving cars still require human supervision and prompt takeover of control when necessary. Poor alertness while controlling self-driving cars could hinder the drivers' ability to intervene during unpredictable situations, thus increasing the risk of avoidable accidents. In this paper we examine the key factors that contribute to drivers' poor alertness, and the potential solutions that have been proposed to address them. Based on this examination we make some recommendations for various stakeholders, such as researchers, drivers, industry and policy makers.

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“…The WoZ method uses a hidden human (i.e., the "wizard") to simulate the role of a computer system (Fraser & Gilbert, 1991). The WoZ method has been adopted in vehicle research to simulate autonomous vehicles and advanced driver assistance systems that do not currently exist or in contrast to which the WoZ approach offers substantial cost savings (Jarosch, Paradies, Feiner, & Bengler, 2019). Application of the WoZ method can occur in driving simulators (Schieben, Heesen, Schindler, Kelsch, & Flemisch, 2009) and with real vehicles on test tracks (Pipkorn, Victor, Dozza, & Tivesten, 2021) and public roads (Jarosch et al, 2019).…”

Section: Wizard-of-oz Methodologymentioning

confidence: 99%

On-Road Assessment of Driver Mode Awareness of Assisted and Automated Driving

Britten

Pérez

2022

Proceedings of the Human Factors and Ergonomics Society Annual

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Increasingly, today’s vehicles offer Society of Automotive Engineers (SAE) Level 2 (L2) technology, and a limited number of SAE Level 3 (L3) automated driving systems (ADSs) are being developed. Since L3 ADSs can operate in L2 and manual driving, drivers’ correct mode awareness is essential. To investigate driver’s mode awareness after transitions between modes, an on-road experiment was conducted using a Wizard-of-Oz method to simulate Assisted Driving (L2-like) and Automated Driving (L3/L4-like). A total of 36 drivers completed the on-road experiment. Participants experienced periods of manual driving, assisted driving, automated driving, and transitions between these modes. After each transition, participants’ mode awareness was measured. It was hypothesized that drivers’ mode awareness would increase with system exposure over the planned automated driving periods. Results showed that drivers exhibited declarative mode awareness during both Assisted (ASD) and Automated Driving (AD) modes.

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Effects of non-driving related tasks in prolonged conditional automated driving – A Wizard of Oz on-road approach in real traffic environment

Cited by 24 publications

References 13 publications

A Wizard of Oz Field Study to Understand Non-Driving-Related Activities, Trust, and Acceptance of Automated Vehicles

A Wizard of Oz Field Study to Understand Non-Driving-Related Activities, Trust, and Acceptance of Automated Vehicles

Self-Driving Cars and Driver Alertness

On-Road Assessment of Driver Mode Awareness of Assisted and Automated Driving

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