Strategies to Assist Drivers in Remaining Attentive While Under Partially Automated Driving: Verification of Human–Machine Interface Concepts

2017 ◽  
Vol 2663 (1) ◽  
pp. 20-26 ◽  
Author(s):  
Robert E. Llaneras ◽  
Brad R. Cannon ◽  
Charles A. Green
Keyword(s):  
Human Machine Interface ◽  
Automated Driving ◽  
Machine Interface

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

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.

Building a Simulation Environment to Test the Human-Machine Interface of an Automated Driving System

2017 ◽  
Vol 2017.15 (0) ◽  
pp. A26
Author(s):  
Masaatsu KUSUNOKI ◽  
Sunkil YUN ◽  
Hidekazu NISHIMURA ◽  
Takaaki TESHIMA ◽  
Mitsuo NISHIMURA
Keyword(s):  
Human Machine Interface ◽  
Simulation Environment ◽  
Automated Driving ◽  
Driving System ◽  
Machine Interface

scholarly journals Test procedure for evaluating the human–machine interface of vehicles with automated driving systems

2019 ◽  
Vol 20 (sup1) ◽  
pp. S146-S151 ◽  
Author(s):  
Frederik Naujoks ◽  
Sebastian Hergeth ◽  
Katharina Wiedemann ◽  
Nadja Schömig ◽  
Yannick Forster ◽  
...  
Keyword(s):  
Test Procedure ◽  
Human Machine Interface ◽  
Automated Driving ◽  
Machine Interface

scholarly journals Context-Adaptive Availability Notifications for an SAE Level 3 Automation

2021 ◽  
Vol 5 (4) ◽  
pp. 16
Author(s):  
Simon Danner ◽  
Alexander Feierle ◽  
Carina Manger ◽  
Klaus Bengler
Keyword(s):  
Driving Simulator ◽  
Human Machine Interface ◽  
Gaze Behavior ◽  
Activation Time ◽  
Automated Driving ◽  
Driving Function ◽  
The Road ◽  
Level 3 ◽  
Machine Interface ◽  
Simulator Study

Context-adaptive functions are not new in the driving context, but even so, investigations into these functions concerning the automation human–machine interface (aHMI) have yet to be carried out. This study presents research into context-adaptive availability notifications for an SAE Level 3 automation in scenarios where participants were surprised by either availability or non-availability. For this purpose, participants (N = 30) took part in a driving simulator study, experiencing a baseline HMI concept as a comparison, and a context-adaptive HMI concept that provided context-adaptive availability notifications with the aim of improving acceptance and usability, while decreasing frustration (due to unexpected non-availability) and gaze deviation from the road when driving manually. Furthermore, it was hypothesized that participants, when experiencing the context-adaptive HMI, would activate the automated driving function more quickly when facing unexpected availability. None of the hypotheses could be statistically confirmed; indeed, where gaze behavior was concerned, the opposite effects were found, indicating increased distraction induced by the context-adaptive HMI. However, the trend in respect to the activation time was towards shorter times with the context-adaptive notifications. These results led to the conclusion that context-adaptive availability notifications might not always be beneficial for users, while more salient availability notifications in the case of an unexpected availability could be advantageous.

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

Information ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 176
Author(s):  
Rebecca Hainich ◽  
Uwe Drewitz ◽  
Klas Ihme ◽  
Jan Lauermann ◽  
Mathias Niedling ◽  
...  
Keyword(s):  
Motion Sickness ◽  
Mitigation Strategy ◽  
Human Machine Interface ◽  
Mitigation Strategies ◽  
Ambient Light ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Test Track ◽  
Physical Discomfort ◽  
Machine Interface

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.

scholarly journals Light-Based External Human Machine Interface: Color Evaluation for Self-Driving Vehicle and Pedestrian Interaction

2019 ◽  
Vol 63 (1) ◽  
pp. 1232-1236 ◽  
Author(s):  
Stefanie M. Faas ◽  
Martin Baumann
Keyword(s):  
Road Traffic ◽  
Design Guidelines ◽  
Human Machine Interface ◽  
Structured Interviews ◽  
Automated Driving ◽  
Wizard Of Oz ◽  
Driving System ◽  
Street Crossing ◽  
Parking Lot ◽  
Machine Interface

In today’s road traffic pedestrians seek eye contact with drivers to move along safely. Such communication is no longer possible with self-driving vehicles. Previous research shows that an external Human-Machine-Interface (eHMI) provides an interface between self-driving vehicles and pedestrians. However, recommendations for standardization are still being developed. The study compares the colors white and turquoise for eHMI lamps indicating that the automated driving system is engaged. The colors are evaluated in a street-crossing scenario and a parking lot scenario with a Wizard-of-Oz vehicle equipped with eHMI lamps mounted on top of the vehicle. We conducted questionnaires and structured interviews with N=59 participants to identify eHMI design guidelines. Our research provides evidence that turquoise facilitates pedestrians’ factors like visibility, discriminability, sense of safety and trust higher than white. The results are consistent among traffic scenarios. This paper contributes in formulating research-based design guidelines to improve pedestrian safety.

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