Effect on Mode Awareness When Changing from Conditionally to Partially Automated Driving

Feldhütter, Anna; Härtwig, Nicolas; Kurpiers, Christina; Hernandez, Julia Mejia; Bengler, Klaus

doi:10.1007/978-3-319-96074-6_34

Cited by 17 publications

(19 citation statements)

References 8 publications

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“…A blue icon on the instrument cluster (taken from [6]) displayed the active automation system status, as did a blue LED strip at the bottom of the windshield (see Figure 2). References [14,[35][36][37][38] suggest displaying the system status additionally using a LED strip. The color blue can be perceived very well peripherally [39] and has been used in previous studies for indicating conditionally or highly automated driving [37,40,41].…”

Section: Human-machine Interface Designmentioning

confidence: 99%

Information Needs and Visual Attention during Urban, Highly Automated Driving—An Investigation of Potential Influencing Factors

et al. 2020

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During highly automated driving, the passenger is allowed to conduct non-driving related activities (NDRA) and no longer has to act as a fallback at the functional limits of the driving automation system. Previous research has shown that at lower levels of automation, passengers still wish to be informed about automated vehicle behavior to a certain extent. Due to the aim of the introduction of urban automated driving, which is characterized by high complexity, we investigated the information needs and visual attention of the passenger during urban, highly automated driving. Additionally, there was an investigation into the influence of the experience of automated driving and of NDRAs on these results. Forty participants took part in a driving simulator study. As well as the information presented on the human–machine interface (system status, navigation information, speed and speed limit), participants requested information about maneuvers, reasons for maneuvers, environmental settings and additional navigation data. Visual attention was significantly affected by the NDRA, while the experience of automated driving had no effect. Experience and NDRA showed no significant effect on the need for information. Differences in information needs seem to be due to the requirements of the individual passenger, rather than the investigated factors.

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Section: Human-machine Interface Designmentioning

confidence: 99%

Information Needs and Visual Attention during Urban, Highly Automated Driving—An Investigation of Potential Influencing Factors

et al. 2020

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“…As a visual HMI element, the instrument cluster, in particular, is considered the primary interaction element for communicating the system state and further detailed information in automated driving [18]. The instrument cluster is used in almost all studies on automated driving (e.g., [8,[19][20][21][22]). In addition, head-up displays have become established that can inform passengers in the primary field of view about system status, emerging situations, and maneuvers without eyes off road (e.g., [8,19,[23][24][25]).…”

Section: Automation Hmi (Ahmi)mentioning

confidence: 99%

“…Augmented reality head-up displays, as an extension of conventional head-up displays, have been used in automated driving because they visualize maneuvers, mark detected objects directly in the environment (e.g., [23,26,27]) and can support the driver in take-over situations (e.g., [28,29]). Due to their good peripheral perception, LED strips are particularly advantageous for communicating changes in the system status (transition between automation modes or to manual driving), and therefore are used, for example, in the windshield, to provide adequate mode and situation awareness (e.g., [8,21,[30][31][32]). However, the monitor in the center console has also been used as part of the aHMI [19].…”

Section: Automation Hmi (Ahmi)mentioning

confidence: 99%

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From HMI to HMIs: Towards an HMI Framework for Automated Driving

et al. 2020

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During automated driving, there is a need for interaction between the automated vehicle (AV) and the passengers inside the vehicle and between the AV and the surrounding road users outside of the car. For this purpose, different types of human machine interfaces (HMIs) are implemented. This paper introduces an HMI framework and describes the different HMI types and the factors influencing their selection and content. The relationship between these HMI types and their influencing factors is also presented in the framework. Moreover, the interrelations of the HMI types are analyzed. Furthermore, we describe how the framework can be used in academia and industry to coordinate research and development activities. With the help of the HMI framework, we identify research gaps in the field of HMI for automated driving to be explored in the future.

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“…That is, a system changes a mode in a more flexible manner depending on a dynamic driving environment, e.g., traffic, climate, and infrastructure. Hence, mode awareness becomes one of the important issues in such adaptive automation (Feldhütter, Härtwig, Kurpiers, Hernandez, & Bengler, 2018;Sarter & Woods, 1995). Feldhütter et al (2018) investigated the visual attention of drivers while a system changed from CA to PA. Their result did not show deterioration in visual attention owing to mode confusion.…”

Section: Introductionmentioning

confidence: 99%

Does Adaptive Mode Transition Contribute to Better Driver Intervention in Highly Automated Driving?

Zhou

Itoh

Kitazaki

2019

Proceedings of the Human Factors and Ergonomics Society Annual

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This paper presents an adaptive mode (level) transition in highly combined driving automation in which the mode of a system could adaptively shift to any level including SAE level 3 (conditional automation, CA) to level 2 (partial automation) based on the driving environment. We show the effects of the adaptive transition on the take over of car control by a human driver and driving behavior after intervention when the system issues a response to intervene. A driving simulator experiment is conducted to collect data during the transition from automated control to manual driving in three scenes: obstacle on a driving lane, blurred lane mark, and stopped car ahead. Results indicate that the interventions of drivers who experience the adaptive transition are delayed in comparison to those who experience only the fixed transition. The adaptive transition is conducive for drivers to stop the car for preventing a potential collision with a stopped car ahead. Owing to the adaptive transition, drivers perceive a critical hazard after taking over car control and provide a rapid response. In addition, during the adaptive transition, drivers prefer verbal messages to the simple “beeping” message.

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Effect on Mode Awareness When Changing from Conditionally to Partially Automated Driving

Cited by 17 publications

References 8 publications

Information Needs and Visual Attention during Urban, Highly Automated Driving—An Investigation of Potential Influencing Factors

Information Needs and Visual Attention during Urban, Highly Automated Driving—An Investigation of Potential Influencing Factors

From HMI to HMIs: Towards an HMI Framework for Automated Driving

Does Adaptive Mode Transition Contribute to Better Driver Intervention in Highly Automated Driving?

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