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Toward Immersive Self-Driving Simulations: Reports from a User Study across Six Platforms

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Author(s)
Yeo, DohyeonKim, GwangbinKim, SeungJun
Type
Conference Paper
Citation
ACM CHI 2020
Issued Date
2020-04-27
Abstract
As self-driving car technology matures, autonomous vehicle research is moving toward building more human-centric interfaces and accountable experiences. Driving simulators avoid many of the ethical and regulatory concerns about self-driving cars and thus play a key role in testing new interfaces or autonomous driving scenarios. However, apart from validity studies for manual driving simulation, the capabilities of driving simulators in replicating the experience of self-driving cars have not been widely investigated. In this paper, we build six self-driving simulation platforms with varying levels of visual and motion fidelities, ranging from a screen-based in-lab simulator to the novel mixed-reality on-road simulator we propose. With a user study, we compare the sense of presence and simulator sickness for each simulator composition, as well as its visual and motion fidelities. Our novel in-vehicle mixed reality simulator showed highest fidelity and presence. Our findings suggest how visual and motion configurations affect experience in autonomous driving simulators.
Publisher
Association for Computing Machinery
Conference Place
US
Honolulu, Hawaii, USA
URI
https://scholar.gist.ac.kr/handle/local/22781
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