A review of automotive human machine interface technologies and techniques to reduce driver distraction

Author(s):  
C.A. Pickering ◽  
K.J. Burnham ◽  
M.J. Richardson
2021 ◽  
Vol 32 (4) ◽  
pp. 4-14
Author(s):  
Christine Mulvihill ◽  
Tim Horberry ◽  
Michael Fitzharris ◽  
Brendan Lawrence ◽  
Raphaela Schnittker ◽  
...  

Recent advances in vehicle technology permit the real-time monitoring of driver state to reduce distraction-related crashes, particularly within the heavy vehicle industry. Relatively little published research has evaluated the human machine interface (HMI) design for these systems. However, the efficacy of in-vehicle technology depends in large part on the acceptability among drivers of the system’s interface. Four variations of the HMI of a prototype multi-modal warning system developed by the authors for driver distraction were evaluated in a truck simulator with eight car drivers and six truck drivers. Driver acceptance of the HMIs was assessed using the System Acceptability Scale; and salience, comprehension and perceived effectiveness of components of the HMIs (modality, intensity of warning) were assessed using likert scales. The results showed that participants considered the HMIs to be acceptable and useful, and that the warning components were largely noticed, understood correctly, and perceived to be effective. Although this study identified no major design flaws with the recently developed HMIs, further simulator testing with a larger sample size is recommended to validate the findings. On-road evaluations to assess the impact of the HMIs on real world safety are a necessary pre-requisite for implementation.


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

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 687
Author(s):  
Jinzhen Dou ◽  
Shanguang Chen ◽  
Zhi Tang ◽  
Chang Xu ◽  
Chengqi Xue

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.


Author(s):  
Saverio Trotta ◽  
Dave Weber ◽  
Reinhard W. Jungmaier ◽  
Ashutosh Baheti ◽  
Jaime Lien ◽  
...  

Procedia CIRP ◽  
2021 ◽  
Vol 100 ◽  
pp. 488-493
Author(s):  
Florian Beuss ◽  
Frederik Schmatz ◽  
Marten Stepputat ◽  
Fabian Nokodian ◽  
Wilko Fluegge ◽  
...  

Nanoscale ◽  
2021 ◽  
Author(s):  
Qiufan Wang ◽  
Jiaheng Liu ◽  
Guofu Tian ◽  
Daohong Zhang

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...


2021 ◽  
Vol 13 (8) ◽  
pp. 188
Author(s):  
Marianna Di Gregorio ◽  
Marco Romano ◽  
Monica Sebillo ◽  
Giuliana Vitiello ◽  
Angela Vozella

The use of Unmanned Aerial Systems, commonly called drones, is growing enormously today. Applications that can benefit from the use of fleets of drones and a related human–machine interface are emerging to ensure better performance and reliability. In particular, a fleet of drones can become a valuable tool for monitoring a wide area and transmitting relevant information to the ground control station. We present a human–machine interface for a Ground Control Station used to remotely operate a fleet of drones, in a collaborative setting, by a team of multiple operators. In such a collaborative setting, a major interface design challenge has been to maximize the Team Situation Awareness, shifting the focus from the individual operator to the entire group decision-makers. We were especially interested in testing the hypothesis that shared displays may improve the team situation awareness and hence the overall performance. The experimental study we present shows that there is no difference in performance between shared and non-shared displays. However, in trials when unexpected events occurred, teams using shared displays-maintained good performance whereas in teams using non-shared displays performance reduced. In particular, in case of unexpected situations, operators are able to safely bring more drones home, maintaining a higher level of team situational awareness.


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