Toward Immersive Self-Driving Simulations: Reports from a User Study across Six Platforms
- Author(s)
- Yeo, Dohyeon; Kim, Gwangbin; Kim, 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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