scholarly journals Dimensional Evolution of Intelligent Cars Human-Machine Interface considering Take-Over Performance and Drivers’ Perception on Urban Roads

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Hao Yang ◽  
Yueran Wang ◽  
Ruoyu Jia
Keyword(s):  
Purchase Intention ◽  
Completion Time ◽  
Human Machine Interface ◽  
Initial Population ◽  
Entropy Theory ◽  
Evolutionary Mechanism ◽  
Traffic Efficiency ◽  
Driving Assistance ◽  
Within Subjects ◽  
Machine Interface

The study analyzed the drivers’ take-over behaviors in intelligent cars when driving on urban roads and tried to find reasonable dimensions of the human-machine interface. Firstly, the main driving assistance functions in the process of take-over were analyzed based on the entropy theory, and the weight values of each function for the consumer’s purchase intention were calculated. Secondly, we explored the perceived comfortable dimensions of the interactive components under typical interaction modes. By means of experiments using a within-subjects design, the initial population of the evolutionary computation was obtained. The evolutionary mechanism of dimensions driven by users’ perception was constructed with a genetic algorithm. After debugging the parameters of the model, we verified the rationality of the model and evolved appropriate dimensions. Finally, the validity of the evolved dimensions was proved by a controlled experiment and paired-sample t-test. The results indicated that the completion time of most take-over tasks under the HMI with the evolved dimensions was significantly shorter, which ensured the HMI could be more conducive to the take-over quality and traffic efficiency.

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.

scholarly journals Human–Machine Interface in Transport Systems: An Industrial Overview for More Extended Rail Applications

Machines ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 36
Author(s):  
Simon Enjalbert ◽  
Livia Maria Gandini ◽  
Alexandre Pereda Baños ◽  
Stefano Ricci ◽  
Frederic Vanderhaegen
Keyword(s):  
Human Factors ◽  
New Technologies ◽  
Human Machine Interface ◽  
Transport Systems ◽  
Special Focus ◽  
Driving Assistance ◽  
Automation Equipment ◽  
Machine Interface ◽  
Transport Modes ◽  
Vehicle Automation

This paper provides an overview of Human Machine Interface (HMI) design and command systems in commercial or experimental operation across transport modes. It presents and comments on different HMIs from the perspective of vehicle automation equipment and simulators of different application domains. Considering the fields of cognition and automation, this investigation highlights human factors and the experiences of different industries according to industrial and literature reviews. Moreover, to better focus the objectives and extend the investigated industrial panorama, the analysis covers the most effective simulators in operation across various transport modes for the training of operators as well as research in the fields of safety and ergonomics. Special focus is given to new technologies that are potentially applicable in future train cabins, e.g., visual displays and haptic-shared controls. Finally, a synthesis of human factors and their limits regarding support for monitoring or driving assistance is proposed.

scholarly journals Eco-Driving Assistance System: A New Way of How to Save Energy

2012 ◽  
Author(s):  
Lydie Nouveliere ◽  
Hong-Tu Luu ◽  
Saïd Mammar ◽  
Qi Cheng ◽  
Olivier Orfila
Keyword(s):  
Energy Consumption ◽  
Energy Saving ◽  
Human Machine Interface ◽  
System A ◽  
Energy Consumption Model ◽  
Driving Assistance ◽  
Machine Interface ◽  
Consumption Model ◽  
Save Energy ◽  
Driving Assistance System

The work developed in this paper is realized within the ecoDriver EU FP7 project that is shortly presented in the first part of the paper. Among the several main objectives underlined by this project, one consists in developing a vehicle and energy consumption model to be validated and then used to help the driver to better drive in terms of consumption and safety, advised by a HMI (Human-Machine Interface) module. Several experimental results are shown to illustrate the obtained energy saving with such an EDAS and the legal speed respect.

Designing the human machine interface in the ATIS domain

1990 ◽  
Author(s):  
B. Bly ◽  
P. J. Price ◽  
S. Park ◽  
S. Tepper ◽  
E. Jackson ◽  
...  
Keyword(s):  
Human Machine Interface ◽  
Machine Interface

scholarly journals Evaluation of Multimodal External Human–Machine Interface for Driverless Vehicles in Virtual Reality

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 687
Author(s):  
Jinzhen Dou ◽  
Shanguang Chen ◽  
Zhi Tang ◽  
Chang Xu ◽  
Chengqi Xue
Keyword(s):  
New Technology ◽  
Pedestrian Safety ◽  
Human Machine Interface ◽  
Visual Features ◽  
Visual Modality ◽  
Human Voice ◽  
Road Users ◽  
Machine Interface ◽  
Interaction Efficiency ◽  
Physical Features

With the development and promotion of driverless technology, researchers are focusing on designing varied types of external interfaces to induce trust in road users towards this new technology. In this paper, we investigated the effectiveness of a multimodal external human–machine interface (eHMI) for driverless vehicles in virtual environment, focusing on a two-way road scenario. Three phases of identifying, decelerating, and parking were taken into account in the driverless vehicles to pedestrian interaction process. Twelve eHMIs are proposed, which consist of three visual features (smile, arrow and none), three audible features (human voice, warning sound and none) and two physical features (yielding and not yielding). We conducted a study to gain a more efficient and safer eHMI for driverless vehicles when they interact with pedestrians. Based on study outcomes, in the case of yielding, the interaction efficiency and pedestrian safety in multimodal eHMI design was satisfactory compared to the single-modal system. The visual modality in the eHMI of driverless vehicles has the greatest impact on pedestrian safety. In addition, the “arrow” was more intuitive to identify than the “smile” in terms of visual modality.

SOLI: A Tiny Device for a New Human Machine Interface

2021 ◽  
Author(s):  
Saverio Trotta ◽  
Dave Weber ◽  
Reinhard W. Jungmaier ◽  
Ashutosh Baheti ◽  
Jaime Lien ◽  
...  
Keyword(s):  
Human Machine Interface ◽  
Machine Interface

scholarly journals Cobots in maxillofacial surgery – challenges for workplace design and the human-machine-interface

Procedia CIRP ◽  
2021 ◽  
Vol 100 ◽  
pp. 488-493
Author(s):  
Florian Beuss ◽  
Frederik Schmatz ◽  
Marten Stepputat ◽  
Fabian Nokodian ◽  
Wilko Fluegge ◽  
...  
Keyword(s):  
Maxillofacial Surgery ◽  
Human Machine Interface ◽  
Workplace Design ◽  
Machine Interface

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...

Sign in / Sign up

Export Citation Format

Share Document