Evaluation of a Human–Machine Interface for Motion Sickness Mitigation Utilizing Anticipatory Ambient Light Cues in a Realistic Automated Driving Setting

Motion sickness (MS) is a syndrome associated with symptoms like nausea, dizziness, and other forms of physical discomfort. Automated vehicles (AVs) are potent at inducing MS because users are not adapted to this novel form of transportation, are provided with less information about the own vehicle’s trajectory, and are likely to engage in non-driving related tasks. Because individuals with an especially high MS susceptibility could be limited in their use of AVs, the demand for MS mitigation strategies is high. Passenger anticipation has been shown to have a modulating effect on symptoms, thus mitigating MS. To find an effective mitigation strategy, the prototype of a human–machine interface (HMI) that presents anticipatory ambient light cues for the AV’s next turn to the passenger was evaluated. In a realistic driving study with participants (N = 16) in an AV on a test track, an MS mitigation effect was evaluated based on the MS increase during the trial. An MS mitigation effect was found within a highly susceptible subsample through the presentation of anticipatory ambient light cues. The HMI prototype was proven to be effective regarding highly susceptible users. Future iterations could alleviate MS in field settings and improve the acceptance of AVs.

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How Can We Improve the Driving Experience with Human-Machine-Interface for Automated Driving?

Advances in Intelligent Systems and Computing - Advances in Usability, User Experience, Wearable and Assistive Technology ◽

10.1007/978-3-030-51828-8_28 ◽

2020 ◽

pp. 218-224

Author(s):

Seda Aydogdu ◽

Miguel Luzuriaga ◽

Bernhard Schick

Keyword(s):

Human Machine Interface ◽

Automated Driving ◽

Driving Experience ◽

Machine Interface

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Use Cases for Assessing, Testing, and Validating the Human Machine Interface of Automated Driving Systems

Proceedings of the Human Factors and Ergonomics Society Annual Meeting ◽

10.1177/1541931218621426 ◽

2018 ◽

Vol 62 (1) ◽

pp. 1873-1877 ◽

Cited By ~ 12

Author(s):

Frederik Naujoks ◽

Sebastian Hergeth ◽

Katharina Wiedemann ◽

Nadja Schömig ◽

Andreas Keinath

Keyword(s):

Traffic Safety ◽

Test Methods ◽

Use Cases ◽

Highway Traffic ◽

Automated Vehicles ◽

Automated Driving ◽

National Highway Traffic Safety ◽

Minimum Requirements ◽

Machine Interface ◽

Increasing Demand

Reflecting the increasing demand for harmonization of human machine interfaces (HMI) of automated vehicles, different taxonomies of use cases for investigating automated driving systems (ADS) have been proposed. Existing taxonomies tend to serve specific purposes such as categorizing transitions between automation modes; however, they cannot be generalized to different systems or combinations of systems. In particular, there is no exhaustive set of use cases that allows entities to assess and validate the HMI of a given ADS that takes into account all possible system modes and transitions. The present paper describes a newly developed framework based on combinatorics of SAE (Society of Automotive Engineers) automation levels that incorporates a comprehensive taxonomy of use cases required for the assessment and validation of ADS HMIs. This forms a much-needed basis for test methods required to verify whether an HMI meets minimum requirements such as those outlined in the National Highway Traffic Safety Administration’s Federal Automated Vehicles policy.

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Efficient Communication of Automated Vehicles and Manually Driven Vehicles Through an External Human-Machine Interface (eHMI): Evaluation at T-Junctions

HCI International 2021 - Posters - Communications in Computer and Information Science ◽

10.1007/978-3-030-78645-8_28 ◽

2021 ◽

pp. 224-232

Author(s):

Hüseyin Avsar ◽

Fabian Utesch ◽

Marc Wilbrink ◽

Michael Oehl ◽

Caroline Schießl

Keyword(s):

Human Machine Interface ◽

Automated Vehicles ◽

Efficient Communication ◽

Machine Interface

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Human–Machine Interface Design and Verification for an Automated Driving System Using System Model and Driving Simulator

IEEE Consumer Electronics Magazine ◽

10.1109/mce.2019.2923899 ◽

2019 ◽

Vol 8 (5) ◽

pp. 92-98

Author(s):

Sunkil Yun ◽

Takaaki Teshima ◽

Hidekazu Nishimura

Keyword(s):

Interface Design ◽

Driving Simulator ◽

Human Machine Interface ◽

System Model ◽

Automated Driving ◽

Driving System ◽

Machine Interface

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Influence of Adaptive Human–Machine Interface on Electric-Vehicle Range-Anxiety Mitigation

Multimodal Technologies and Interaction ◽

10.3390/mti4010004 ◽

2020 ◽

Vol 4 (1) ◽

pp. 4

Author(s):

Antonyo Musabini ◽

Kevin Nguyen ◽

Romain Rouyer ◽

Yannis Lilis

Keyword(s):

Electric Vehicle ◽

Autonomous Vehicle ◽

Human Machine Interface ◽

Traffic Light ◽

Automated Driving ◽

Range Anxiety ◽

Driving Task ◽

Machine Interface ◽

Emergency Stop ◽

Save Energy

The electrification of vehicles is without a doubt one of the milestones of today’s automotive technology. Even though industry actors perceive it as a future standard, acceptance, and adoption of this kind of vehicles by the end user remain a huge challenge. One of the main issues is the range anxiety related to the electric vehicle’s remaining battery level. In the scope of the H2020 ADAS&ME project, we designed and developed an intelligent Human Machine Interface (HMI) to ease acceptance of Electric Vehicle (EV) technology. This HMI is mounted on a fake autonomous vehicle piloted by a hidden joystick (called Wizard of Oz (WoZ) driving). We examined 22 inexperienced EV drivers during a one-hour driving task tailored to generate range anxiety. According to our protocol, once the remaining battery level started to become critical after manual driving, the HMI proposed accurate coping techniques to inform the drivers how to reduce the power consumption of the vehicle. In the following steps of the protocol, the vehicle was totally out of battery, and the drivers had to experience an emergency stop. The first result of this paper was that an intelligent HMI could reduce the range anxiety of the driver by proposing adapted coping strategies (i.e., transmitting how to save energy when the vehicle approaches a traffic light). The second result was that such an HMI and automated driving to a safe spot could reduce the stress of the driver when an emergency stop is necessary.

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A Taxonomy of Strategies For Supporting Time-Sharing With Non-Driving Tasks in Automated Driving

Proceedings of the Human Factors and Ergonomics Society Annual Meeting ◽

10.1177/1071181319631283 ◽

2019 ◽

Vol 63 (1) ◽

pp. 2088-2092

Author(s):

Dengbo He ◽

Dina Kanaan ◽

Birsen Donmez

Keyword(s):

Driver Distraction ◽

Mitigation Strategies ◽

Future Research ◽

Time Sharing ◽

Automated Vehicles ◽

Automated Driving ◽

Negative Effects ◽

Spare Capacity ◽

Control Automation ◽

Driving Task

Driver distraction is one of the leading causes of vehicle crashes. The introduction of higher levels of vehicle control automation is expected to alleviate the negative effects of distraction by delegating the driving task to automation, thus enabling drivers to engage in non-driving-related tasks more safely. However, before fully automated vehicles are realized, drivers are still expected to play a supervisory role and intervene with the driving task if necessary while potentially having more spare capacity for engaging in non-driving-related tasks. Traditional distraction mitigation perspectives need to be shifted for automated vehicles from mainly preventing the occurrence of non-driving-related tasks to dynamically coordinating time-sharing between driving and non-driving-related tasks. In this paper, we provide a revised and expanded taxonomy of driver distraction mitigation strategies, discuss how the different strategies can be used in an automated driving context, and propose directions for future research in supporting time-sharing in automated vehicles.

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Mitigating Motion Sickness in Automated Vehicles with Vibration Cue System

10.36227/techrxiv.18092492 ◽

2022 ◽

Author(s):

Daofei Li ◽

Linhui Chen

Keyword(s):

Motion Sickness ◽

Driving Simulator ◽

Ride Comfort ◽

System Development ◽

Road Transport ◽

Automated Vehicles ◽

Automated Driving ◽

User Friendliness ◽

Practical Applications ◽

Vehicle Movement

Motion sickness is very common in road transport. To guarantee ride comfort and user experience, there is an urgent need for effective solutions to motion sickness mitigation in semi- and fully-automated vehicles. Considering both effectiveness and user-friendliness, a vibration cue system is proposed to inform passengers of the upcoming vehicle movement through tactile stimulation. By integrating the motion planning results from automated driving algorithms, the vibration cueing timing and patterns are optimized with the theory of motion anticipation. Using a cushion-based prototype of vibration cue system, 20 participants were invited to evaluate this solution in two conditions of driving simulator experiments. Results show that with the proposed vibration cue system, it could also help participants to comprehend the cues and to generate motion anticipation. The participants’ motion sickness degrees were significantly lowered. This research may serve as one foundation for the detailed system development in practical applications.(This article has been accepted for publication in Ergonomics, published by Taylor & Francis.)

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