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.

scholarly journals A temporal analysis of safety drivers taking back control in public roadway automated driving trials

2019 ◽  
Vol 63 (1) ◽  
pp. 1532-1536
Author(s):  
Nicole M. Corcoran ◽  
Daniel V. McGehee ◽  
T. Zachary Noonan
Keyword(s):  
Autonomous Vehicle ◽  
Temporal Analysis ◽  
Automated Vehicles ◽  
Automated Driving ◽  
The Public ◽  
Environment Research ◽  
Operational Design ◽  
Take Back ◽  
Driving Task ◽  
Over Time

In 2019, industry is in the testing stages of level 4 SAE/NHTSA automated vehicles. While in testing, L4 vehicles require a safety driver to monitor the driving task at all times. These specially trained drivers must take back control if the vehicle doesn’t seem to be responding correctly to the ever-changing roadway and environment. Research suggests that monitoring the driving task can lead to a decrease in vigilance over time. Recently, Waymo publicly released takeover request and mileage data on its 2018 L4 autonomous vehicle takeover requests. From this data, which was represented in mileage, we created temporal metric which showed that there were typically 150-250 hours without a takeover request. From this we suggest that there may be a decrement in vigilance for Waymo safety drivers. While there are still many unknowns, we suggest Waymo release takeover requests in terms of time rather than mileage and provide more information on the operational design domains of these vehicles. Expanding the content of this publicly-released data could then give researchers and the public more understanding of the conditions under which safety drivers are functioning.

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 Designing the Human Machine Interface to Address Range Anxiety

2012 ◽  
Vol 5 (1) ◽  
pp. 72-82 ◽  
Author(s):  
Tawhid Khan ◽  
Mark Williams ◽  
Tom Wellings ◽  
Duncan Robertson ◽  
Jackie Binersley
Keyword(s):  
Human Machine Interface ◽  
Range Anxiety ◽  
Machine Interface

Participatory Design in the Classroom: Exploring the Design of an Autonomous Vehicle Human-Machine Interface with a Visually Impaired Co-Designer

2020 ◽  
Vol 64 (1) ◽  
pp. 1921-1925
Author(s):  
Earl W. Huff ◽  
Kathryn M. Lucaites ◽  
Aminah Roberts ◽  
Julian Brinkley
Keyword(s):  
Participatory Design ◽  
Low Vision ◽  
Autonomous Vehicle ◽  
Human Machine Interface ◽  
Motor Vehicles ◽  
Visual Disability ◽  
Personal Mobility ◽  
Machine Interface ◽  
Design Ideas

Self-driving vehicles are the latest innovation in improving personal mobility and road safety by removing arguably error-prone humans from driving-related tasks. Such advances can prove especially beneficial for people who are blind or have low vision who cannot legally operate conventional motor vehicles. Missing from the related literature, we argue, are studies that describe strategies for vehicle design for these persons. We present a case study of the participatory design of a prototype for a self-driving vehicle human-machine interface (HMI) for a graduate-level course on inclusive design and accessible technology. We reflect on the process of working alongside a co-designer, a person with a visual disability, to identify user needs, define design ideas, and produce a low-fidelity prototype for the HMI. This paper may benefit researchers interested in using a similar approach for designing accessible autonomous vehicle technology.

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.

Sign in / Sign up

Export Citation Format

Share Document