Authors:

Xipeng Wang

Date Created:

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About Paper:

The development of safer and more efficient driving systems has gained attention in the fields of automation and importantly, human-automation interaction. It is widely held that autonomous vehicles with embedded models of human cognition may be better able to interact with human passengers or drivers and improve overall safety and comfort. Therefore, this research aims to understand how humans interact with autonomous vehicles, with a particular emphasis on modeling how humans' trust, mental workload, risk perception, and self-confidence change in response to different stimuli. The goal is to develop an SAE Level II autonomous vehicle simulator which will serve as a testbed for human subjects' experiments. The backbone of the high- fidelity 3D simulator is developed using Unreal Engine 5 and encompasses an expansive open world map set in a dynamic city environment. The simulator includes intricately designed traffic systems that mimic real-life scenarios and gives experimenters complete control over independent variables such as automation transparency, task complexity, recommended control mode, and system reliability. Various optimization techniques are applied to ensure optimal performance of the software and hardware. Pilot studies will be conducted to evaluate the ecological validity of the developed scenarios and to inform any required modifications. Future work involves data collection through experimental trials and using the collected data for gray-box system identification and validation.

Source:

Purdue University / 2023

Topics:

No topics listed

Co-authors:

Xipeng Wang