Negative Effect of External Human-Machine Interfaces in Automated Vehicles on Pedestrian Crossing Behaviour: A Virtual Reality Experiment

2021 ◽  
pp. 718-725
Author(s):  
Jieun Lee ◽  
Tatsuru Daimon ◽  
Satoshi Kitazaki
Keyword(s):  
Virtual Reality ◽  
Automated Vehicles ◽  
Human Machine Interfaces ◽  
Negative Effect ◽  
Pedestrian Crossing

scholarly journals External Human-Machine Interfaces on Automated Vehicles: Effects on Pedestrian Crossing Decisions

2019 ◽  
Vol 61 (8) ◽  
pp. 1353-1370 ◽  
Author(s):  
Koen de Clercq ◽  
Andre Dietrich ◽  
Juan Pablo Núñez Velasco ◽  
Joost de Winter ◽  
Riender Happee
Keyword(s):  
Automated Vehicles ◽  
Urban Road ◽  
Head Mounted Display ◽  
Road Users ◽  
Human Machine Interfaces ◽  
Pedestrian Crossing ◽  
Vehicle Size

Objective: In this article, we investigated the effects of external human-machine interfaces (eHMIs) on pedestrians’ crossing intentions. Background: Literature suggests that the safety (i.e., not crossing when unsafe) and efficiency (i.e., crossing when safe) of pedestrians’ interactions with automated vehicles could increase if automated vehicles display their intention via an eHMI. Methods: Twenty-eight participants experienced an urban road environment from a pedestrian’s perspective using a head-mounted display. The behavior of approaching vehicles (yielding, nonyielding), vehicle size (small, medium, large), eHMI type (1. baseline without eHMI, 2. front brake lights, 3. Knightrider animation, 4. smiley, 5. text [WALK]), and eHMI timing (early, intermediate, late) were varied. For yielding vehicles, the eHMI changed from a nonyielding to a yielding state, and for nonyielding vehicles, the eHMI remained in its nonyielding state. Participants continuously indicated whether they felt safe to cross using a handheld button, and “feel-safe” percentages were calculated. Results: For yielding vehicles, the feel-safe percentages were higher for the front brake lights, Knightrider, smiley, and text, as compared with baseline. For nonyielding vehicles, the feel-safe percentages were equivalent regardless of the presence or type of eHMI, but larger vehicles yielded lower feel-safe percentages. The Text eHMI appeared to require no learning, contrary to the three other eHMIs. Conclusion: An eHMI increases the efficiency of pedestrian-AV interactions, and a textual display is regarded as the least ambiguous. Application: This research supports the development of automated vehicles that communicate with other road users.

Designing Tomorrow’s Human-Machine Interfaces in Autonomous Vehicles: An Exploratory Study in Virtual Reality

2020 ◽  
pp. 151-160
Author(s):  
Sebastian Stadler ◽  
Henriette Cornet ◽  
Danqing Huang ◽  
Fritz Frenkler
Keyword(s):  
Virtual Reality ◽  
Exploratory Study ◽  
Autonomous Vehicles ◽  
Human Machine Interfaces

scholarly journals Will Automated Vehicles Negatively Impact Traffic Flow?

2017 ◽  
Vol 2017 ◽  
pp. 1-17 ◽  
Author(s):  
S. C. Calvert ◽  
W. J. Schakel ◽  
J. W. C. van Lint
Keyword(s):  
Traffic Flow ◽  
Gradual Transition ◽  
Mixed Traffic ◽  
Limited Data ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Low Level ◽  
Negative Effect ◽  
Vehicle Automation ◽  
Capacity Drop

With low-level vehicle automation already available, there is a necessity to estimate its effects on traffic flow, especially if these could be negative. A long gradual transition will occur from manual driving to automated driving, in which many yet unknown traffic flow dynamics will be present. These effects have the potential to increasingly aid or cripple current road networks. In this contribution, we investigate these effects using an empirically calibrated and validated simulation experiment, backed up with findings from literature. We found that low-level automated vehicles in mixed traffic will initially have a small negative effect on traffic flow and road capacities. The experiment further showed that any improvement in traffic flow will only be seen at penetration rates above 70%. Also, the capacity drop appeared to be slightly higher with the presence of low-level automated vehicles. The experiment further investigated the effect of bottleneck severity and truck shares on traffic flow. Improvements to current traffic models are recommended and should include a greater detail and understanding of driver-vehicle interaction, both in conventional and in mixed traffic flow. Further research into behavioural shifts in driving is also recommended due to limited data and knowledge of these dynamics.

scholarly journals How Do eHMIs Affect Pedestrians’ Crossing Behavior? A Study Using a Head-Mounted Display Combined with a Motion Suit

Information ◽  
2019 ◽  
Vol 10 (12) ◽  
pp. 386 ◽  
Author(s):  
Lars Kooijman ◽  
Riender Happee ◽  
Joost de Winter
Keyword(s):  
Urban Environment ◽  
Motion Tracking ◽  
Automated Vehicles ◽  
Forward Motion ◽  
The Road ◽  
Head Mounted Display ◽  
Human Machine Interfaces ◽  
Do So

In future trac, automated vehicles may be equipped with external human-machine interfaces (eHMIs) that can communicate with pedestrians. Previous research suggests that, during first encounters, pedestrians regard text-based eHMIs as clearer than light-based eHMIs. However, in much of the previous research, pedestrians were asked to imagine crossing the road, and unable or not allowed to do so. We investigated the effects of eHMIs on participants’ crossing behavior. Twenty-four participants were immersed in a virtual urban environment using a head-mounted display coupled to a motion-tracking suit. We manipulated the approaching vehicles’ behavior (yielding, nonyielding) and eHMI type (None, Text, Front Brake Lights). Participants could cross the road whenever they felt safe enough to do so. The results showed that forward walking velocities, as recorded at the pelvis, were, on average, higher when an eHMI was present compared to no eHMI if the vehicle yielded. In nonyielding conditions, participants showed a slight forward motion and refrained from crossing. An analysis of participants’ thorax angle indicated rotation towards the approaching vehicles and subsequent rotation towards the crossing path. It is concluded that results obtained via a setup in which participants can cross the road are similar to results from survey studies, with eHMIs yielding a higher crossing intention compared to no eHMI. The motion suit allows investigating pedestrian behaviors related to bodily attention and hesitation.

Interaction between pedestrians and automated vehicles: Exploring a motion-based approach for virtual reality experiments

2021 ◽  
Vol 82 ◽  
pp. 316-332
Author(s):  
Janina Bindschädel ◽  
Ingo Krems ◽  
Andrea Kiesel
Keyword(s):  
Virtual Reality ◽  
Automated Vehicles

scholarly journals Taming the eHMI jungle: A classification taxonomy to guide, compare, and assess the design principles of automated vehicles' external human-machine interfaces

2020 ◽  
Vol 7 ◽  
pp. 100174 ◽  
Author(s):  
Debargha Dey ◽  
Azra Habibovic ◽  
Andreas Löcken ◽  
Philipp Wintersberger ◽  
Bastian Pfleging ◽  
...  
Keyword(s):  
Design Principles ◽  
Automated Vehicles ◽  
Human Machine Interfaces
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