Effects of External Human Machine Interfaces on Automated Vehicles' Communicative Interactions With Human Drivers

Master Thesis (2021)
Author(s)

Shiva Nischal Lingam (TU Delft - Civil Engineering & Geosciences)

Contributor(s)

Haneen Farah – Mentor (TU Delft - Transport and Planning)

B. van van Arem – Mentor (TU Delft - Transport and Planning)

J.C.F. Winter – Mentor (TU Delft - Human-Robot Interaction)

Yongqi Dong – Mentor (TU Delft - Transport and Planning)

Anastasia Tsapi – Mentor (Royal HaskoningDHV)

Faculty
Civil Engineering & Geosciences
Copyright
© 2021 Shiva Nischal Lingam
More Info
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Publication Year
2021
Language
English
Copyright
© 2021 Shiva Nischal Lingam
Graduation Date
30-11-2021
Awarding Institution
Delft University of Technology
Project
['SAMEN']
Programme
['Civil Engineering | Transport and Planning']
Faculty
Civil Engineering & Geosciences
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Abstract

Driving involves communicative interactions where human drivers use communication signals to negotiate their right-of-way for road safety. The introduction of automated vehicles (AVs) in mixed-traffic environment, where human drivers will interact with AVs, will affect the nature of these communicative interactions. AVs and human-driven vehicles (HDVs) use different communication forms (e.g., vehicle-to-vehicle communication between AVs vs eye-contact between humans). This raises road safety concerns. AVs might need a communication system that conveys intent to HDVs, clearly. This research investigates the potential of external human machine interfaces (eHMIs) in AV-HDV interactions at T-intersections to improve communicative interactions. Traditional traffic signals were used as inspiration to develop two eHMIs concepts, one placed on vehicle while the other on infrastructure. eHMI on vehicle might require less visual scanning from the interacting human to know AV intent and speed, but on the other hand, the AV might not be in driver field-of-vision compared to an eHMI on the infrastructure. The effects of eHMIs were investigated using a driving simulator with forty-six participants. The results show that both eHMIs had a significant and positive effect on driver trust, acceptance, and emotions. Drivers were calmer with eHMI placed on the infrastructure than on the vehicle. Both eHMIs reduced the time to maximum braking of human drivers and increased their compliance with AVs. The eHMI on vehicle reduced critical interactions, measured by the Post Encroachment Time, between AVs and HDVs. It is concluded that eHMIs can improve AV-HDV communicative interactions at T-intersections. No significant differences were observed between the eHMI conditions in participants’ preference and efficiency of the AV-HDV interactions, measured by human driver compliance. Hence, this research recommends further investigation of eHMIs in different on-road interactions.

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