Investigating the Design of Information Presentation in Take-Over Requests in Automated Vehicles

Colley, Mark; Gruler, Lukas; Woide, Marcel; Rukzio, Enrico

doi:10.1145/3447526.3472025

Cited by 9 publications

(4 citation statements)

References 51 publications

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“…To achieve a power of 0.95 with 𝛼 = 0.05, 37 participants should result in a small effect size (0.15 [110]) in a withinsubjects ANOVA. Originally, 62 individuals were recruited via Prolific (a platform to find participants [282], already used in automotive research, e.g., [70]), but 14 had to be excluded due to failed attention checks (e.g., Anchored interaction locations: e) seat, f) floor, g) door, h) table, i) rear, and j) ceiling.…”

Section: Resultsmentioning

confidence: 99%

A Design Space for Human Sensor and Actuator Focused In-Vehicle Interaction Based on a Systematic Literature Review

Jansen

Colley

Rukzio

2022

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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Automotive user interfaces constantly change due to increasing automation, novel features, additional applications, and user demands. While in-vehicle interaction can utilize numerous promising modalities, no existing overview includes an extensive set of human sensors and actuators and interaction locations throughout the vehicle interior. We conducted a systematic literature review of 327 publications leading to a design space for in-vehicle interaction that outlines existing and lack of work regarding input and output modalities, locations, and multimodal interaction. To investigate user acceptance of possible modalities and locations inferred from existing work and gaps unveiled in our design space, we conducted an online study (N=48). The study revealed users' general acceptance of novel modalities (e.g., brain or thermal activity) and interaction with locations other than the front (e.g., seat or table). Our work helps practitioners evaluate key design decisions, exploit trends, and explore new areas in the domain of in-vehicle interaction.

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Section: Resultsmentioning

confidence: 99%

A Design Space for Human Sensor and Actuator Focused In-Vehicle Interaction Based on a Systematic Literature Review

Jansen

Colley

Rukzio

2022

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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“…This list is then used by SituHMI to direct the focus of attention to the most important element. In SituWare we tested different interaction methods for that purpose [1].…”

Section: A Modular System Implemented In Rosmentioning

confidence: 99%

Assessing Drivers' Situation Awareness in Semi-Autonomous Vehicles: ASP based Characterisations of Driving Dynamics for Modelling Scene Interpretation and Projection

Suchan,

Osterloh

2023

Electron. Proc. Theor. Comput. Sci.

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Semi-autonomous driving, as it is already available today and will eventually become even more accessible, implies the need for driver and automation system to reliably work together in order to ensure safe driving. A particular challenge in this endeavour are situations in which the vehicle's automation is no longer able to drive and is thus requesting the human to take over. In these situations the driver has to quickly build awareness for the traffic situation to be able to take over control and safely drive the car. Within this context we present a software and hardware framework to asses how aware the driver is about the situation and to provide human-centred assistance to help in building situation awareness. The framework is developed as a modular system within the Robot Operating System (ROS) with modules for sensing the environment and the driver state, modelling the driver's situation awareness, and for guiding the driver's attention using specialized Human Machine Interfaces (HMIs).A particular focus of this paper is on an Answer Set Programming (ASP) based approach for modelling and reasoning about the driver's interpretation and projection of the scene. This is based on scene data, as well as eye-tracking data reflecting the scene elements observed by the driver. We present the overall application and discuss the role of semantic reasoning and modelling cognitive functions based on logic programming in such applications. Furthermore we present the ASP approach for interpretation and projection of the driver's situation awareness and its integration within the overall system in the context of a real-world use-case in simulated as well as in real driving.

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“…In the previous literature, the most frequently used factors to classify different types of TOC were an initiator (e.g., driver-initiated vs. system-initiated; Colley et al, 2021;Lu et al, 2016;Lv et al, 2018;Maggi et al, 2022;McCall et al, 2019;Morando et al, 2020) and a direction of transition (or a receiver) (e.g., vehicle to driver vs. driver to vehicle; Colley et al, 2021;Hock et al, 2016;Jarosch & Bengler, 2019;Lu et al, 2016;Lv et al, 2018;Maggi et al, 2022;McCall et al, 2019). Table 2 represents four types of TOC using initiator and direction based on the previous TOC taxonomy (Lu et al, 2016;Lu & de Winter, 2015).…”

Section: Toc Taxonomy Using Initiator and Directionmentioning

confidence: 99%

“…Planned vs. unplanned TOC. In terms of takeover, a planned, or scheduled, TOC occurs when the driving automation approaches the end of its operational design domain (ODD), whereas an unplanned, or unscheduled, TOC occurs when the driving automation encounters immediate and unforeseen situations that the driving automation cannot handle (e.g., unexpected obstacle in the road or adverse weather conditions; Colley et al, 2021). Regarding takeovers, this frame may have similar implications compared to the time budget.…”

Section: Toc Taxonomy Using Predictabilitymentioning

confidence: 99%

Incorporating driver expectations into a taxonomy of transfers of control for automated vehicles

Lee

Koenig

et al. 2022

Proceedings of the Human Factors and Ergonomics Society Annual

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This study expands existing taxonomies for transfer of control (TOC) with driving automation. A TOC taxonomy is necessary to categorize types of TOC, interpret drivers’ behavior during a transition, and assess the safety implications of each TOC type. However, existing taxonomies do not capture important aspects of the driver’s reaction to the transfer because they often only focus on the initiator and receiver. Thus, we synthesized relevant literature and suggested factors for expanding a TOC taxonomy. The literature synthesis revealed that temporal attributes (e.g., urgency) and predictability of TOC are dimensions that can extend existing taxonomies. Based on the findings, we developed a new TOC taxonomy using four factors (initiator, receiver, system predictability, and driver expectation) with relevant use cases. The new taxonomy distinguishes 16 different types of TOCs and enhances our understanding of TOC types and their implications for safety.

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Investigating the Design of Information Presentation in Take-Over Requests in Automated Vehicles

Cited by 9 publications

References 51 publications

A Design Space for Human Sensor and Actuator Focused In-Vehicle Interaction Based on a Systematic Literature Review

A Design Space for Human Sensor and Actuator Focused In-Vehicle Interaction Based on a Systematic Literature Review

Assessing Drivers' Situation Awareness in Semi-Autonomous Vehicles: ASP based Characterisations of Driving Dynamics for Modelling Scene Interpretation and Projection

Incorporating driver expectations into a taxonomy of transfers of control for automated vehicles

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