scholarly journals Challenges and Opportunities in Developing Next Generation In-Vehicle HMI Systems

2019 ◽  
Vol 63 (1) ◽  
pp. 2115-2116
Author(s):  
Rebecca Williamson ◽  
Yu Zhang ◽  
Bruce Mehler ◽  
Ying Wang
Keyword(s):  
Artificial Intelligence ◽  
Human Factors ◽  
Market Share ◽  
User Behavior ◽  
Autonomous Driving ◽  
Human Machine Interface ◽  
Next Generation ◽  
Challenges And Opportunities ◽  
Machine Interface ◽  
User Friendly

The next generation of automotive human machine interface (HMI) systems is expected to be heavily dependent upon artificial intelligence; from autonomous driving to speech assistance, from gesture & touch-enabled interfaces to web & mobile integration. Smooth, safe, and user-friendly interaction between the driver and the vehicle is a key to winning market share. This panel aims to discuss challenges and opportunities for the next generation of automotive HMI from the perspective of human factors and user behavior. Panelists from industry and academia will offer their unique perspectives on the concerns and opportunities in developing future in-vehicle HMIs.

Co@N-CNT/MXene in situ grown on carbon nanotube film for multifunctional sensors and flexible supercapacitor

Nanoscale ◽  
2021 ◽  
Author(s):  
Qiufan Wang ◽  
Jiaheng Liu ◽  
Guofu Tian ◽  
Daohong Zhang
Keyword(s):  
Artificial Intelligence ◽  
Carbon Nanotube ◽  
Energy Storage ◽  
Storage Systems ◽  
Rapid Development ◽  
Human Machine Interface ◽  
Key Factors ◽  
Flexible Supercapacitor ◽  
Machine Interface

The rapid development of human-machine interface and artificial intelligence is dependent on flexible and wearable soft devices such as sensors and energy storage systems. One of the key factors for...

Adapting to the User

2010 ◽  
pp. 1006-1020
Author(s):  
Matthias Jöst
Keyword(s):  
Human Factors ◽  
Human Machine Interface ◽  
Aviation Industry ◽  
Personal Lives ◽  
Machine Interface ◽  
Human Factors Research

Pervasive computers cover many areas of both our working and personal lives. This chapter investigates this phenomenon through the human factors impacts, particularly in aviation. This chapter gives a brief introduction to pervasive computers and how they interact with aviation human factors research. Further, it follows the discussion on how pervasive computers have permeated into an integral part of the aviation industry by reviewing the specific issues of cockpit automation and the human-machine interface in the aircraft cockpit.

Outline of the Human Factor Elements Evident with Pervasive Computers

2011 ◽  
pp. 330-342
Author(s):  
Genevieve Watson
Keyword(s):  
Human Factors ◽  
Human Factor ◽  
Human Machine Interface ◽  
Aviation Industry ◽  
Personal Lives ◽  
Machine Interface ◽  
Human Factors Research

Pervasive computers cover many areas of both our working and personal lives. This chapter investigates this phenomenon through the human factors impacts, particularly in aviation. This chapter gives a brief introduction to pervasive computers and how they interact with aviation human factors research. Further, it follows the discussion on how pervasive computers have permeated into an integral part of the aviation industry by reviewing the specific issues of cockpit automation and the human-machine interface in the aircraft cockpit.

scholarly journals Human–Machine Interaction in Driving Assistant Systems for Semi-Autonomous Driving Vehicles

Electronics ◽  
2021 ◽  
Vol 10 (19) ◽  
pp. 2405
Author(s):  
Heung-Gu Lee ◽  
Dong-Hyun Kang ◽  
Deok-Hwan Kim
Keyword(s):  
Autonomous Vehicles ◽  
Driving Simulator ◽  
Response Speed ◽  
Autonomous Driving ◽  
Human Machine Interface ◽  
Driver Assistance Systems ◽  
Human Machine Interaction ◽  
Driving Assistance ◽  
Assistant Systems ◽  
Machine Interface

Currently, the existing vehicle-centric semi-autonomous driving modules do not consider the driver’s situation and emotions. In an autonomous driving environment, when changing to manual driving, human–machine interface and advanced driver assistance systems (ADAS) are essential to assist vehicle driving. This study proposes a human–machine interface that considers the driver’s situation and emotions to enhance the ADAS. A 1D convolutional neural network model based on multimodal bio-signals is used and applied to control semi-autonomous vehicles. The possibility of semi-autonomous driving is confirmed by classifying four driving scenarios and controlling the speed of the vehicle. In the experiment, by using a driving simulator and hardware-in-the-loop simulation equipment, we confirm that the response speed of the driving assistance system is 351.75 ms and the system recognizes four scenarios and eight emotions through bio-signal data.

Development of an advanced human–machine interface for next generation nuclear power plants

1999 ◽  
Vol 64 (1) ◽  
pp. 109-126 ◽  
Author(s):  
Soon Heung Chang ◽  
Seong Soo Choi ◽  
Jin Kyun Park ◽  
Gyunyoung Heo ◽  
Han Gon Kim
Keyword(s):  
Nuclear Power ◽  
Power Plants ◽  
Nuclear Power Plants ◽  
Human Machine Interface ◽  
Next Generation ◽  
Machine Interface

Human Factors in Future Vertical Lift, the U.S. Army’s Next Generation Rotorcraft

2020 ◽  
Vol 64 (1) ◽  
pp. 149-153
Author(s):  
Katie Ernst ◽  
Michael Wilson ◽  
David Durbin ◽  
Roth Roth ◽  
Laura Militello ◽  
...  
Keyword(s):  
Human Factors ◽  
United States Army ◽  
The United States ◽  
Next Generation ◽  
Work Requirements ◽  
Design And Implementation ◽  
Mission Success ◽  
Challenges And Opportunities ◽  
Successful Design ◽  
Vertical Lift

The United States Army is developing and procuring their next generation of rotorcraft, called the Future Vertical Lift (FVL) program. FVL rotorcraft will fly faster and incorporate new work requirements such as command of unmanned systems and optionally piloted variants. Successful design and implementation of human automation teaming will be paramount to the mission success and safety of FVL systems, presenting both challenges and opportunities for human factors personnel supporting FVL development. The purpose of this panel is to highlight the range of human factors challenges within the FVL program. Panelists are drawn from both government and industry, and represent a diverse set of perspectives including user, researcher, and practitioner. The session will include time for panelists to introduce their work and challenges that they are addressing within the FVL program as well as interact with audience members.

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