Abstract

With the further development of highly automated vehicles, drivers will engage in non-related tasks while being driven. Still, drivers have to take over control when requested by the car. Here, the question arises, how potentially distracted drivers get back into the control-loop quickly and safely when the car requests a takeover. To investigate effective human–machine interactions, a mobile, versatile, and cost-efficient setup is needed. Here, we describe a virtual reality toolkit for the Unity 3D game engine containing all the necessary code and assets to enable fast adaptations to various human–machine interaction experiments, including closely monitoring the subject. The presented project contains all the needed functionalities for realistic traffic behavior, cars, pedestrians, and a large, open-source, scriptable, and modular VR environment. It covers roughly 25 km2, a package of 125 animated pedestrians, and numerous vehicles, including motorbikes, trucks, and cars. It also contains all the needed nature assets to make it both highly dynamic and realistic. The presented repository contains a C++ library made for LoopAR that enables force feedback for gaming steering wheels as a fully supported component. It also includes all necessary scripts for eye-tracking in the used devices. All the main functions are integrated into the graphical user interface of the Unity® editor or are available as prefab variants to ease the use of the embedded functionalities. This project’s primary purpose is to serve as an open-access, cost-efficient toolkit that enables interested researchers to conduct realistic virtual reality research studies without costly and immobile simulators. To ensure the accessibility and usability of the mentioned toolkit, we performed a user experience report, also included in this paper.

Highlights

  • Research on the interactions between humans and cars is mostly done with stationary simulators

  • We describe LoopAR as a modular toolkit to test a takeover of control in critical traffic situations from automated cars to human drivers by combining

  • Its current state and design provide a promising, new, low-cost, and mobile setup to conduct studies that were traditionally only done in stationary simulators

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Summary

Introduction

In the upcoming years, when automated vehicles of SAE (society of automotive engineers) automation levels 3 and 4 will emerge, the demands on the driver’s cognitive system will alter radically, as the role of humans as continuously active decision-makers in vehicles is replaced by automated systems [3,4]. Such techniques include the Audi traffic jam pilot [5] or Tesla’s full self-driving beta [6]. The reliability of highly automated systems is primarily determined by the driver’s cognitive processes, meaning how fast a safe transition to manual drive is possible [8]

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