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Computer Science > Human-Computer Interaction

arXiv:2204.13755 (cs)
[Submitted on 28 Apr 2022 (v1), last revised 4 Oct 2022 (this version, v2)]

Title:Hybrid Eyes: Design and Evaluation of the Prediction-level Cooperative Driving with a Real-world Automated Driving System

Authors:Chao Wang, Derck Chu, Marieke Martens, Matti Krüger, Thomas H. Weisswange
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Abstract:Currently, there are still various situations in which automated driving systems (ADS) cannot perform as well as a human driver, particularly in predicting the behaviour of surrounding traffic. As humans are still surpassing state-of-the-art ADS in this task, a new concept enabling human driver to help ADS to better anticipate the behaviour of other road users was developed. Preliminary results suggested that the collaboration at the prediction level can effectively enhance the experience and comfort of ADS. For an in-depth investigation of the concept, we implemented an interactive prototype, called Prediction-level Cooperative Automated Driving system (PreCoAD), adapting an existing ADS that has been previously validated on the public road. The results of a driving simulator study among 15 participants in different highway scenarios showed that PreCoAD could enhance automated driving performance and provide a positive user experience. Follow-up interviews with participants also provided insights into the improvement of the system.
Comments: 13pages, 7 figures
Subjects: Human-Computer Interaction (cs.HC)
Cite as: arXiv:2204.13755 [cs.HC]
  (or arXiv:2204.13755v2 [cs.HC] for this version)
  https://doi.org/10.48550/arXiv.2204.13755
Related DOI: https://doi.org/10.1145/3543174.3546832

Submission history

From: Chao Wang [view email]
[v1] Thu, 28 Apr 2022 20:13:37 UTC (14,310 KB)
[v2] Tue, 4 Oct 2022 14:19:39 UTC (13,908 KB)
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